Cassava (Manihot esculenta Crantz) is a crop of nutritional and economic importance worldwide, cultivated in more than 100 tropical and subtropical countries including Ecuador, traditionally cultivated in its three continental regions: the Amazon, the Coast and in the valleys of the Sierra. The purpose of this study was to characterize 195 accessions from INIAP's Ecuadorian cassava collection through 1) morphological characterization with qualitative and quantitative descriptors; and 2) ecogeographic characterization to know the climatic, geophysical and edaphic conditions in which cassava grows and which environments are frequent or marginal for its cultivation. For the morphological characterization, 27 morphological descriptors were used (18 qualitative and nine quantitative), and for the ecogeographic characterization, 55 variables (41 climatic, two geophysical and 12 edaphic). As a result, four morphological groups and three ecogeographic groups were identified. In the research, morphological variability was evidenced, mainly in descriptors related to the leaf, stems and inflorescences. In addition, it was possible to identify accessions that can adapt to extreme conditions of drought and poor soils, which could be used for improvement.

Background: As the opioid epidemic continues, understanding the geospatial, temporal and demand patterns is important for policymakers to assign resources and interdict individual, organization, and country-level bad actors. Methods: GIS geospatial-temporal analysis and extreme-gradient boosted random forests evaluate ICD-10 F11 opioid-related admissions and admission rates using geospatial analysis, demand analysis, and explanatory models, respectively. The period of analysis was January 2016 through September 2018. Results: The analysis shows existing high opioid admissions in Chicago and New Jersey with emerging areas in Atlanta, Salt Lake City, Phoenix, and Las Vegas. High rates of admission (claims per 10,000 population) exist in the Appalachian area and on the Northeastern seaboard. Explanatory models suggest that hospital overall workload and financial variables might be used for allocating opioid-related treatment funds effectively. Gradient-boosted random forest models accounted for 87.8% of the variability of claims on blinded 20% test data. Conclusions: Based on the GIS analysis, opioid admissions appear to have spread geographically, while higher frequency rates are still found in some regions. Interdiction efforts require demand-analysis such as that provided in this study to allocate scarce resources for supply-side and demand-side interdiction: prevention, treatment, and enforcement. Based on GIS analysis, the opioid epidemic is likely to spread or diffuse through the country, and interdiction efforts require demand-analysis such as that provided in this study to allocate scarce resources for supply-side and demand-side interdiction: prevention, treatment, and enforcement.

Semantic Web technologies are being increasingly adopted by the geospatial community during last decade through the utilization of open standards for expressing and serving geospatial data. This was also dramatically assisted by an ever increasing access and usage of geographic mapping and location-based services via smart devices in people’s daily activities. In this paper we explore the developmental framework of a pure Javascript client-side GIS platform exclusively based on invocable geospatial Web services. We also extend Javascript utilization on the server side by deploying a node server acting as a bridge between open source WPS libraries and popular geoprocessing engines. The vehicle for such an exploration is a cross platform Web browser capable of interpreting Javascript commands to achieve interaction with geospatial providers. The tool is a generic Web interface providing capabilities of acquiring spatial datasets, composing layouts and applying geospatial processes. In an ideal form the end-user will have to identify those services, which satisfy a geo-related need and put them in the appropriate row. The final output may act as a potential collector of freely available geospatial web services. Its server-side components may exploit geospatial processing suppliers composing that way a light-weight fully transparent open Web GIS platform.

Flood events are becoming more severe, causing significant problems to human communities, including physical, psychological, and material damage. For both flood forecasting in emergency response situations and flood mapping, georeferencing and data curation are paramount in the context of prevention or preparedness. Hence, data display, data management, and articulation with numerical simulation results must occur on GIS platforms. Our research is motivated by recent advances in Web and GIS technologies, social sensing and high-performance computing, and an envisaged wider availability of remote sensing data. This paper presents and discusses an innovative Web GIS platform named "RiverCure Portal" or "RCP" for short. This platform combines observations and hydrodynamic modelling tools to support various stages of the flood risk management cycle, including operational response, emergency preparedness, and risk assessment. RCP is a multi-organisation, multi-context digital platform with flexible configuration features to define and support multiple sensor types and modelling options, satisfying the various needs of different organisations and stakeholders. In addition, this paper discusses the RiverCure Approach, which encompasses the following tasks directly supported by the RCP platform: defining the context and involved geometries, associating sensors to the context, pre-processing and generating the context mesh, defining the simulation event, running the simulation event, and analysing the results from the simulation event. Thus, the RCP streamlines and simplifies data analysis and simulation procedures to meet decision-makers' needs. The novelties discussed in this paper include the design and discussion of a Web GIS platform that allows (i) to manage flood data and results of simulations at several contextual levels by different stakeholders such as domain experts, decision-makers, researchers, or students; (ii) to process and curate sensed data obtained from physical and social sensors; and (iii) update the state and values of the parameters of simulation tools through continuous data assimilation techniques for forecasting purposes. Finally, this paper supports the explanation and discussion with a running example, "Águeda 2016 flood" event, which dataset is publicly available for further study and experimentation.

This study analyzed the dynamic spatial and temporal distribution of natural disasters in Traditional Authorities Ngabu and Lundu in Chikwawa District in the Lower Shire Valley. The study assessed the vulnerability of people to disasters, evaluated the value of losses and damages incurred when disasters strike and to locate disaster hotspots. The mixed design that embraces qualitative and quantitative approaches was used. Data collection methods such as in-depth interviews, Focus Group Discussion, documentary review, structured interview and Participatory Geographical Information Systems (P-GIS) were employed. The study revealed that farming with 64%, 37% and 76% done before, during and after disasters respectively, was the main adaptive capacity to floods and drought in T/A Ngabu. In T/A Lundu, community members also valued farming with 58%, 55% and 70% done before, during and after disasters respectively. The study found that Traditional Environmental Knowledge Systems (TEKS) were highly valued in T/As Ngabu with 90% and Lundu 81%. The study also unveiled those disasters led to losses of human lives and livestock and damages to properties, environment and critical infrastructures. However, some community members still remained in the disaster-prone areas for farming, fishing, protecting the chieftaincy and ancestral reasons. The study also showed that CBDRM was highly adopted. However, disasters continued to occur and losses were increasing. Generally, CBDRM could be effective through multisectoral approach and good legal institutional framework. Therefore, the research recommends that further studies similar to this, should use GIS in assessing the efficacy of CBDRM approaches to produce hazard maps that will be used as models for disaster risk reduction in Chikwawa District in the Lower Shire Valley.

The current context has proved unequivocally that public health is one of the pillars of society, playing a determining role and being a balancing factor. This study aimed to provide additional evidence supporting the use of geographic information systems in and from public health. For the study, the authors use public data from the Transparency COVID-19 data set at the Romanian level, which they process with the QGIS application, to effectively determine the influence of the location of the vaccination centers, respectively the impact on the incidence of COVID 19. The research carried out, which generated 153 maps, allows the statement, that reports based on the geographical location of data collected from the public health system are easier to analyze and to evaluate the effectiveness of the decisions taken. By analyzing the evolution of some public health indicators, that are difficult to correlate through analytical relationships, this study offers, as the main finding, an additional argument for the use of geographic information systems in public health management. As the limitations of research in the simulation component are relevant, further research is needed to provide the necessary evidence to convince about the effectiveness of using geographic information systems to optimize outcomes in the public health system. For this reason, it is necessary that the data structures in the public system, to be built and collected in a comprehensive way, with an optimal level of granularity.

Wind energy is one of the most attractive renewable energy sources because of its low operating, maintenance, and production costs as well as its low environmental impact. The goal of this study is to discover the best locations in Bangladesh where wind farms can be built and operated efficiently. This study applied the GIS and AHP methodologies to examine the eight important parameters upon which the suitability of locations is highly dependent. This analysis finds that Bangladesh has large regions appropriate for wind farm installation, with 3718.76 km² and 16631.14 km² classified as "very high" and "high" suitability, respectively. It was also observed that wind speed, land slope, and elevation each had a height-weighted criterion of 32 %, 27 %, and 12 %, respectively, when picking suitable locations. However, the viability of this study in identifying suitable sites has been evaluated based on ROC and AUC techniques and found satisfactory as per AUC value. The knowledge gained from this study will help the sustainable and renewable energy development authority (SREDA), Bangladesh to expedite the renewable energy investment process and will ensure the great certainty. The findings of this research can be considered as baseline information in the wind energy sector.

Remote sensing technology, especially using satellite images, has become essential support in many aspects of decision-making, particularly in disaster risk management. It requires a shorter period of data updates and less cost compared to conventional field observations and surveys. Yet, the intensive processing and high-powered computing resources are necessary to analyze satellite imagery data through Geographic Information System (GIS). In this paper, we introduce the identification and mapping of natural disaster impact in Indonesia using the open-source collaborative tool of Google Earth Engine (GEE) application which analyzes the relative temporal difference of Earth surface from three major satellite images: Sentinel-1, Sentinel-2, and Landsat-8. Taking the advantage of the geographical, geological, and demographic conditions of Indonesia's disaster-prone areas, we analyze relative difference from normalized difference vegetation index (NDVI) out of months before and after natural disaster occurrence to measure the impact of natural disaster in focus study areas. Given the high-vegetation nature of three main natural disaster impacted areas in Indonesia: Aceh, Palu, and Yogyakarta, we are able to simplify the analysis by highlighting areas with vegetative loss or gain after the event. Using an open-source GEE application, namely HazMapper, we identify and visualize the aftermath of the tsunami disaster in Aceh and Palu as well as the earthquake in Yogyakarta. Our study is potentially beneficial for government and decision-makers to utilize publicly available satellite images for disaster recovery and mitigation policy.

Highway alignment is an essential part of the highway planning and design phase. Highways are impacted by existing projects and surrounding context. The isolation of geo-technical analysis from highway planning and design also delays the planning process. This study therefore proposes a model that integrates building information modelling (BIM) and geographic information science (GIS) capabilities to facilitate the planning and design process. Semantic web technologies are used to integrate BIM and GIS data on a semantic level. The proposed model also helps to identify geohazards by providing geological analysis. The visualization of the proposed project can help reduce design errors and miscommunication, which, in turn, reduces project risk. In addition, the model facilitates highway alignment optimization by incorporating visualization, simulation, and analysis into the planning and design phase. The proposed model provides future opportunities for project professionals to have organized, reliable and dynamic ways to manage the project during construction.

Building Information Modelling (BIM) is increasingly adopted in supporting Facility Management (FM). However, in the future perspective of efficiently encouraging the management of building estates, e.g. owned by public authorities or institutions, providing an effective integration between Building Information Models and Geographic Information Systems (GIS) becomes also essential. This contribution is therefore aimed at presenting a methodological framework integrating BIM and GIS environments by mainly taking advantage of open-source tools (e.g. QGIS and Dynamo) and non-proprietary data exchange formats like the COBie (Construction-Operations Building Information Exchange) international standard. Unlike what has been mainly proposed in the literature concerning BIM-GIS integration so far, the methodology presented in this paper does not provide for an integral transfer of BIM data to the GIS platform. The work led instead to the development of an effective bidirectional integration between the two informative systems, by properly relating them and allowing for an easy switch from one system database to the other. This integrated framework is intended to enable facility managers to easily visualize in the GIS platform, through queryable 2D and 3D maps, some relevant information from BIM to efficiently manage workspaces. At the same time, this hybrid informative system allows BIM practitioners to access and manage more detailed information stored in Building Information Models. The findings of experimental applications of the proposed methodological approach to the ex-Macciotta pediatric hospital, a healthcare building owned by the University of Cagliari, are finally presented and discussed.

Landfills pollute air, soil, surface and groundwater worldwide. Present work aims to assessment environmental Risks three landfills in southern Riyadh using GIS, soil quality guidelines, and contamination indices. GIS tools indicated an increase in the area of the landfill sites with time. The concentration of heavy metals (HMs) in the investigated landfills had the following descending order: Fe (11532 mg/kg) ˃ Al (5405 mg/kg) ˃ Pb (561.7 mg/kg) ˃ Zn (356.8 mg/kg) ˃ Mn (165 mg/kg) ˃ Cr (74.8 mg/kg) ˃ Cu (42.7 mg/kg) ˃ Ni (22.4 mg/kg) ˃ V (21.8 mg/kg) ˃ As (5.16 mg/kg) ˃ Co (4.08 mg/kg). The highest values of Al, As, Co, Ni, Pb, V, and Zn were recorded in Riyadh landfill 3, at the third industrial city. However, average values of all HMs decrease those from most worldwide soils and background, except Zn, Cu, Cr, and Pb. Results of enrichment factor and statistical analysis indicated deficiency to minimal enrichment and geogenic sources for Al, Co, Mn, and V, while those of As, Cr, Pb, Zn, and Ni showed EF ˃ 2 implied anthropogenic activities, especially in in Riyadh landfill 3. Additionally, very high contamination and high effects range-median were reported in individual samples, especially for Pb, As, and Zn, indicated frequent adverse effects for these HMs. The difference in contamination for the HMs in the studied landfill sites might be attributed to the difference in the magnitude of input for each metal into the landfill site and/or the difference in the removal rate of each metal from it.

Abstract: The empirical soil loss model, RUSLE, was used with a conjunction of remotely sensed data and geographic information system technology to delineate the soil erosion and watershed priorities in terms of conservation practices at seven boundary sub-watersheds (labeled as SW-00, SW-01, …, SW-06) between Iraq and Iran at Ali Al-Gharbi area, southern Iraq. The six factors of the RUSLE model, ie. the rainfall erosivity, the soil erodibility, the slope steepness length, the crop management, and management practice, were calculated or estimated using information from different data sources such as remotely sensed data and previous studies. The finding showed that the annual soil erosion loss ranges from 0 - 1890 (tons h-1 y-1) with an average of 0.66 (tons h-1 y-1). Values of soil erosion were classified into five classes: very low, low, moderate, high, and very high. The potential soil loss in the high and very high classes ranges from 14.84 to 1890 (tons h-1 y-1), and these classes occupy only 27 km2 of the study area, indicating that the soil loss is very low in the area being examined. In terms of the spatial distribution of soil loss, the northern and northeastern parts (mountains and hills) of the sub-watersheds where the slope is steeper are more likely to erode than the plain area in the southern and southeastern portions, indicating that slope, in addition to rainfall erosivity, has a dominant effect on the soil erosion rate. The study of soil erosion in the watersheds under consideration reveals that only the northern portions of the SW-00, SW-02, and SW-04 watersheds require high priority conservation plans; however, these portions are primarily located in mountain regions, making conservation plans implementation in these areas impractical. Due to low soil loss, other sub-watersheds, particularly SW-01, SW-03, SW-05, and SW-06, are given low priority.

A number of research projects and a rich literature have dealt with the theme of abandoned medieval villages in Sardinia since the end of the 60s of the last century. Some more or less precise catalogues and reviews of villages in limited territories have been published. Only recently, however, this subject is being addressed in an interdisciplinary manner, combining traditional historical research with the results of archaeological surveys and excavation campaigns, geo-archaeology, toponymy, paleoclimatology. This allows us to have a picture of the landscape and human settlement evolution with its historical changes, conditioned not only by institutional superstructures but also by human and natural traumatic events. Particular attention will be given to the sudden changes that occurred between the thirteenth and fourteenth centuries. To carry out this survey, it is possible to use some very powerful IT tools which, through the aggregation, organization, correlation and management of information, allow the geo-localization of abandoned villages as proven by the documentary evidence. Thus, on this documentation, existing and acquired in the future, is founded the construction of the related information system. The most easy and suitable tools for this purpose are the CMSs (Content Management Systems) which, in association with GIS (Geographic Information Systems) engines, allow spatial and contextual analysis of the settlements, as they were inserted in their territory. This type of tools aggregates different peculiarities of the object of study, supporting a multidisciplinary reading on the argument. The computerized tools, integrated as a system, offer the possibility to implement it, feeding it and correcting it continuously, basing on new acquisitions. In this study, we will examine a historical areal, for which we have a sufficient number of sources available: Sarrabus, Colostrai and Quirra, adding to the geographical visual information, the temporal visual evolution.

The article reports morphotectonic and petrological characteristics of magmatic systems of Permo-Triassic traps in the sedimentary cover of the Siberian Craton, Taimyr, as well as in the crystalline basement of the Mesozoic cover of the West Siberian Plate and the Kara sedimentary basin based on the relief analysis, seismotomography data, magnetic and gravitational anomalies. Four development sectors of magma-permeable zones were distinguished along the perimeter of the craton of the Anabar Shield. The western sector is characterized by an extensive stretching area, on which the lava region of volcanic ridges junction of the Tunguska syncline formed. A striking feature of subaerial volcanism is the meridional petrochemical trend of increasing the silica of basic magmas in intrusive rocks of the Siberian Craton while the volumes of embedded melts are reduced.

Irrigation water management components evaluation is mandatory for sustainable irrigated agriculture production in the era of water scarcity. In this research spatio-temporal distribution of irrigation water components were evaluated at canal command area in Indus Basin Irrigation System (IBIS) using remote sensing based geo-informatics approach. Satellite derived MODIS product-based Surface Energy Balance Algorithm for Land (SEBAL) was used for the estimation of the Actual Evapotranspiration (ETa). Satellite derived SEBAL based ETa was calibrated and validated using the ground data-based advection aridity method (AA). Statistical analysis of the SEBAL based ETa and AA shows the mean 87.1 mm and 47.9 mm and, 100 mm and 77 mm, Standard deviation of 27.7 mm and 15.9 mm and, 34.9 mm and 16.1 mm, R of 0.93 and 0.94, NSE of 0.72 and 0.85, PBIASE -12.9 and -4.4, RMSE 34.9 and 5.76 for the Kharif and Rabi season, respectively. Rainfall data was acquired from the Tropical Rainfall Measuring Mission (TRMM). TRMM based rainfall was calibrated with the point observatory data of the Pakistan Metrological Department Stations. Canal water data was collected from the Punjab Irrigation department for the assessment of canal water availability. Water The water balance approach was applied in the unsaturated zone for the quantification of the gross and net Groundwater irrigation. Mmonthly variation of ETa with the minimum average value of 63.3 mm in January and the maximum average value of 110.6 mm in August was found. While, the average annual of four cropping years (2011-12 to 2014-15) ETa was found 899 mm. Average of the sum of Net Canal Water Use (NCWU) and Rainfall during the study period of four years was only 548 mm (36% of ETa) and this resulted the 739.6 mm of groundwater extraction. While the annual based variation in groundwater extraction of 632 mm and 780 mm was found. Seasonal analysis revealed 39% and 61% of groundwater extraction proportion during Rabi and Kharif season, respectively. The variation in four cropping year’s monthly groundwater extraction was found 28.7 mm to 120.3 mm. This variation was high in the 2011-12 to 2012-13 cropping year (0 mm to 148.7 mm), dependent upon the occurrence of rainfall and crop phenology. Net groundwater irrigation, estimated after incorporating the efficiencies was 503 mm year-1 on average for the four cropping years.

Here we introduce Literature Mapper, a Python QGIS plugin that provides a method for creating a spatial bibliography manager as well as a specification for storing spatial data in a bibliography manager. Literature Mapper uses QGIS’ spatial capabilities to allow users to add location information to a Zotero library, a free and open source bibliography manager. Literature Mapper enhances the citations in a user’s online Zotero database with geo-locations by storing spatial coordinates as part of traditional citation entries. Literature Mapper receives data from and sends data to the user’s online database via Zotero’s web API. By using Zotero as the backend data storage, Literature Mapper benefits from all of its features including shared citation Collections, public sharing, and an open web API usable by additional applications, such as web mapping libraries. We evaluate Literature Mapper’s ability to provide insights into the spatial distribution of published literature by mapping the study sites described in academic publications related to California’s coastal strand vegetation. The results of this exercise are presented in static and web map form.

House is the haven that keeps people from natural and human conditions, it gives them trust, safety, and steadiness. It is one of the most basic human needs this became a serious function which cities offer, and became one of the most important aspects which caught urban researchers interest, they take into consideration a wide range of architectural, social, and economic indicators. The study aims to provide an overall conception of Rwandz residential functions, using a collection of parameters and some GIS and statistical techniques, to help establish plans and future projects to improve the growth of this city and other towns and cities in that area. The study found that the old parts of Rwandz city which are located in the core, differ from the outer parts which are relatively newer in many properties, generally, the core is more densely populated than the outer, bigger family size, more illiteracy, and unemployment, few incomes, older houses, smaller houses, in the opposite of the outer parts. Besides, the study tested the correlation coefficient between the criteria; it found some strong statistical relationships between them, which reflected some real-life properties of the residential function. Lastly, the study designed a regression model to predict the main residential function criteria.

The province of Macerata, Italy, is a topographically complex region which has been little studied in terms of its temperature and precipitation climatology. Temperature data from 81 weather stations and precipitation data from 55 rain gauges were obtained, and, following quality control procedures, were investigated on the basis of 3 standard periods: 1931-1960, 1961-1990 and 1991-2014. Spatial and temporal variations in precipitation and temperature were analysed on the basis of six topographic variable (altitude, distance from the sea, latitude, distance from the closest river, aspect, and distance from the crest line). Of these, the relationship with altitude showed the strongest correlation. Use of GIS software allowed investigation of the most accurate way to present interpolations of these data and assessment of the differences between the 3 investigated periods. The results of the analyses permit a thorough evaluation of climate change spatially over the last 60 years. Generally, the amount of precipitation is diminished while the temperature is increased across the whole study area, but with significant variations within it. Temperature increased by 2 to 3°C in the central part of the study area, while near the coast and in the mountains the change is between about 0 and 1°C, with small decreases focused in the Appennine and foothill belt (-1 to 0°C). For precipitation, the decrease is fairly uniform across the study area (between about 0-200 mm), but with some isolated areas of strong increase (200-300 mm) and only few parts of territory in which there is an increase of 0-200 mm, mainly in the southern part of the coast, to the south-west and inland immediately behind the coast. The monthly temperature trend is characterized by a constant growth, while for precipitation there is a strong decrease in the amount measured in January, February and October (between 25 and 35 mm on average).

Groundwater is an important role of the environment in natural resources. The major sources of groundwater contamination in this study were open discharges of domestic sewage, inadequate sewerage system, open defecation, septic tanks, soak pits, contaminated water pools, unorganized solid waste dumping and use of fertilizers, pesticides for agriculture deteriorated the condition. In this present study revealed that the physical and chemical characteristics of ground water in different areas of Kannur district in Kerala have been determined different seasons with respect to its suitability for drinking and agricultural purposes. For this study the groundwater samples were collected during pre-monsoon and post-monsoon seasons from 70 wells representing the entire the study area. The groundwater samples were analyzed for Physico-chemical characteristics using standard techniques in laboratory and compared with standards. The samples were analyzed with reference to the WHO and BIS standards. The groundwater quality information of the entire study area have been prepared using statistical and GIS technique for all the parameters. This paper proved in GIS will be helpful for measuring, monitoring and managing the groundwater pollution in the study area and suggested to protect groundwater resources in the environment.

Cloudburst is one of the most devastating and frequently occurring natural hazardous events in Indian Himalayan region. Localized deep cumulus convective clouds have a capability of giving enormous amount of rainfall over a limited horizontal area, within a short span of time. Whenever, such events occur, lead to flash floods causing landslides, house collapses, dislocation of traffic, and human casualties on a large scale. Therefore, it is necessary to predict the cloudburst inundation zones accurately to avoid damage associated with them. For this, high resolution Digital Elevation Model generated from CartoSat-1 (Stereo pair) were integrated in MIKE 11 Hydrodynamic 1D model to generate longitudinal profile of the study area and to find water level, peak discharge, flow velocity, flow width at different reaches along the Asi ganga and Bhagirathi river, to know the Cloudburst flood inundation scenario. On 3^rd August 2012 one of the major Cloudburst event occurred in Asi Ganga Valley in Indian Himalayan region which was considered for simulation of hydrodynamic model. For a Cloudburst event, 100 mm/hr rainfall was considered for the simulation of the hydrodynamic model. It is observed that the discharge rise from 50 m³/s to 549.164 m³/s (an abrupt increase of about 10 times) within 1 hr at Sangamchetty in Asiganga river and at Joshiyara area rise from 600 m³/s to 3378.69 m³/s (an abrupt increase of about 5 times) within 4 hr in Bhagirathi river. Similarly the water level rises around 3 m and 6m in Asi Ganga and Bhagirathi rivers respectively. Flash Flood inundation areas due to Cloudburst on 3^rd August 2012 were demarcated from the simulation results in GIS environment.

Web-Based GIS is a platform that deals with the representation, management and storage of locational information for real-time online retrieval, viewing and manipulations. Web-based GIS incorporate all the functions of information systems: gathering, storing, retrieving, visualizing and dissemination of spatial information on the Internet. In modern-day GIS serve as vital means to not only geo-referenced locational information that are been retrieved but also the development in web-based technologies as well as there involvements in decision making processes. In recent time the increased used of GIS on the Internet have totally transformed and made three distinct changes across our GIS environment. These are: (i) how are GIS data being accessed (ii) what are modes of its transmission and (iii) what available GIS functionalities are within the environment for analysis. This paper examines the applications and technologies associated with web-based GIS architectures with an emphasis on the internet GIS based structure and case studies of GIS Web-based applications in MapInfo, Microsoft Silverlight Technology and the ArcGIS Server using ArcGIS JavaScript API.

Electric energy has become essential nowadays not only for the daily life of each of us but also for the economy of different countries. The dissemination of geographic information plays an important role in national development as it facilitates communication between managers, investors, and consumers in this sector. Since the management of electricity network data was previously done in Tunisia based on paper maps and plans, the purpose of this article is to present a case of planning based on GIS, Web, and 3D Web GIS, which would have significant positive consequences on this sector from a technical and financial sides with an improvement in customer satisfaction and the creation of an intelligent electricity network which will be a real decision-making tool. This work draws up an inventory of the network MV (Medium Voltage)/LV (Low Voltage) of the region of Medjez El Bab which routes electricity to the big centers of consumption with access to MV/LV subscribers. The analysis of the network's impedance allowed carrying out different scenarios to optimize performance and obtain more realistic routes. Many thematic maps were produced as part of this project (Slope map, Land use map, map of the MV voltage domains, map of the MV/LV transformer stations power, etc.). A three-dimensional virtual city has been developed to visualize the graphical and attribute data for the study area. A Web and 3D Web GIS applications that allows the publication of the interactive maps on the Web as well as the database information have been developed to offer users the possibility of consulting the produced products by internet. Finally, a website related to the study was developed.

Spatial planning processes generally consider three levels of planning, which are applied to three types of territory: state, county and city. As the coastal areas are of a significant natural, cultural, economic and social value, as well as are characterized by a diverse range of involved society with specific interests and needs, there is a necessity for an innovative and new approach to sustainable development planning in accordance with the modern age of growth, as well as to work with local communities in specific areas. Planning of a small populated area like village territory is more diverse and subject to the wishes and needs of the population. Small territory planning involves a very narrow circle of individuals or communities that identify spatial development needs for the future, including socio-economic, cultural, and environmental and climate change scenarios. In order to assess the development opportunities and needs of the area, it is necessary to monitor the area by regularly updating data. As it is well known, methodically derived data (facts) provide objectivity and transparency. Nowadays, when information about the present and the past is circulating very fast, it is possible to analyze the current situation, to forecast the future using databases, and to show several constructed realities (scenarios) using the geographic information system (GIS). Therefore, it is crucial to explore and find out the local needs-based planning approach to the development of village in coastal areas.

With the development of the times, the objective requirement of CIM is to establish a 3D digital city. This article proposes a software front-end and back-end open source solution for the production and release of 3D modeling in different industries. This article is based on the construction practice of CIM basic platform database, and analyzes the spatial resolution and corresponding entity expression effect of 3D models in CIM basic platform from the perspective of CIM industry application, as well as the advantages and disadvantages of application. It also elaborates on the classification of 3D models and the geographic elements and buildings they contain, and introduces the relationship between 3D models and vector data, raster data, attribute data, as well as the difficulties of modeling technology. On this basis, large-scale 3D modeling and mapping experiments were conducted in the power industry, water industry, construction industry, and indoormap scene. The article has improved and optimized the algorithms for 3D excavation volume and wire azimuth, and introduced various parametric modeling techniques. From the perspective of project application and user feedback, the modeling quality, performance, and user experience have been recognized by the market. This article has certain reference value for the construction of the 3D model basic framework, theme oriented model system, modeling technology selection, and the release of massive 3D data services in the CIM basic platform construction.

The main purpose of this study was to create a prototype of an unmanned aerial system equipped with intelligent hardware and software technologies necessary for monitoring the health and growth of crops in orchards. Another important objective was to use low-cost sensors that accurately measure ultraviolet solar radiation. The device, which needs to be attached to the commercial DJI Mini 4 Pro drone, should be small in size, portable, and have very low energy consumption. For this purpose, the widely used Vishay VEML6075 digital optical sensor was selected and implemented in a prototype, alongside a Raspberry Pi Zero 2W minicomputer. To collect data from these sensors, a program written in Python was used, containing specific blocks for data acquisition from each sensor, to facilitate the monitoring of ultraviolet (UV) radiation, or battery current. By analyzing the data obtained from the sensors, several important conclusions are drawn that may provide valuable pathways for the further development of mobile or modular equipment. Furthermore, the results of the plant condition analysis with proposed models in the Geographic Information System (GIS) environment were also presented. The visualization of maps indicating variations in vegetation condition led to the identification of problem areas like hydric stress.

Geographic information systems (GIS) are critical for mapping the geographical distribution of illness prevalence, disease transmission patterns, and spatially modeling environmental elements of disease occurrence. This study was purposively to map and cluster five diseases such as cholera, diabetes, malaria, asthma and hypertension using GIS techniques in Southern Malawi. Getis-Ord Gi* statistic was used to determine spatial distribution of diseases. Data were acquired from Malawi Demographic Health Survey (DHS) and assessed geographically using spa-tial-statistical analytical techniques, including the kernel and hotspot models. These models were developed to investigate and exhibit the dissimilar patterns of the selected diseases, as well as to depict places of high concentration, and they may be useful in understanding local patterns of diseases and their geographical correlations. The study found that asthma is high in Mangochi, Balaka, Blantyre, Thyolo, Mulanje, Chikwawa and Nsanje districts. It was also revealed that hypertension patients were highly concentrated in Blantyre, Thyolo, Chikwawa, Zomba, Mangochi and Nsanje Districts. Mangochi, Phalombe, Nsanje and Chikwawa are high risk areas of Malaria and diabetes is highest in Blantyre. Cholera is high in Mangochi, Balaka, Blantyre, Thyolo, Mu-lanje, Chikwawa and Nsanje districts.

In the context of climate change, the need for stakeholders to contribute to achieving SDG2 is no longer in doubt especially in sub-Saharan Africa. In this study of the landscape within 10 km of the Donomadé model farm, southeastern Togo, we sought to assess vegetation health in ecosystems and agrosystems, including their capacity to produce biomass for agroecological practices. Sentinel-2 sensor data from 2015, 2017, 2020, and 2022 were preprocessed and used to calculate normalized vegetation fire ratio index (NBR), vegetation fire severity index (dNBR), and CASA-SEBAL models. From these different analyses, it was found that vegetation stress increased across the landscape depending on the year of the time series. We estimated that 9952.215 ha, 10,397.43 ha, and 9854.90 ha were highly stressed in 2015, 2017, and 2020, respectively. Analysis of the level of interannual severity revealed the existence of highly photosynthetic areas which had experienced stress. These areas, which were likely to have been subjected to agricultural practices, were estimated to be 8704.871 ha (dNBR2017–2015), 8253.17 ha (dNBR2020–2017), and 7513.93 ha (dNBR2022–2020). In 2022, the total available biomass estimated by remote sensing for was 3,741,715 ± 119.26 kgC/ha/y. The annual average was 3401.55 ± 119.26 kgC/ha/y. In contrast, the total area of healthy vegetation was estimated to be 4594.43 ha, 4301.30 ha, and 4320.85 ha, in 2015, 2017, and 2022, respectively. The acceptance threshold of the net primary productivity (NPP) of the study area was 96%. The coefficient of skewness (0.81 ± 0.073) indicated a mosaic landscape. Productive and functional ecosystem components were present, but these were highly dispersed. These findings suggest a great opportunity to promote agroecological practices. Mulching may be an excellent technique for enhancing overall ecosystem services as targeted by the SDGs, by means of reconversion of plant biomass consumed by vegetation fires or slash-and-burn agricultural practices.

Aquifer water is the key staple to develop successful sustainable agricultural activity in newly reclaimed areas in Egypt. Monitoring irrigation water quality is necessary for a proper planning of this exhaustible resources. This work aims to evaluate the groundwater quality in the western newly reclaimed areas of Beni-Suef governorate. Fifty aquifer water sample were collected from wells scattered in the arid western Beni-Suef fringes during May of 2022. Water chemical analyses were conducted to estimate the parameters of Irrigation Water Quality Index (IWQI) namely the Electrical Conductivity (EC), Sodium (Na+), Chloride (Cl-), Bicarbonate (HCO3-) and the Adjusted percentage of Absorbed Sodium (SARo). The Inverse Distance Weighted (IDW) technique used to map the spatial distribution of aquifer water chemical characteristics and IWQI. The results showed that EC values were high in the south of the study area and decreased at its north and east borders. The results showed that the average of dissolved cations and anions were 236.4, 102, 77.5, 106.2, 309.6, 148.1 and 113 meq/l for Ca2+, Mg2+, Na+, K+, SO42-, HCO3-, and Cl-, respectively. Regarding to IWQI, its' values ranged from 28 to 81. Two-thirds of the study area classified as having moderate limitations for sustainable irrigation purposes. Furthermore, one-thirds of the area classified as having high restrictions.

Groundwater vulnerability assessment has become a useful tool for groundwater pollution pre-vention. Groundwater vulnerability maps provide useful data to protect groundwater resources. The identification of agricultural patterns is an important issue for optimized land management. The Tagus river watershed is the backbone of this survey. Naturtejo UNESCO Global Geopark, in central inland Portugal, corresponds to a rural territory. Intensive agricultural practices showed a rising tendency in the last decades. The most internationally used method for vulnerability evaluation is the DRASTIC index. In this survey, the DRASTICAI index is introduced. A new at-tribute - Anthropogenic Influence - is here added. Five levels of growing vulnerability, from low to high, can be here acknowledged. Idanha-a-Nova municipality is the most affected by intensive farming activities. A robust assessment of groundwater quality has a key role. Climate change scenarios and water scarcity are important issues in years to come. Therefore, optimized groundwater management is essential to consider in policy-making strategies.

This paper presents the validation of the End Point Rate (EPR) tool for QGIS (EPR4Q), a tool built-in QGIS Graphical Modeler to calculate the shoreline change by End Point Rate method. The EPR4Q tries to fill the gap of user-friendly and free open-source tool for shoreline analysis in Geographic Information System environment, since the most used software - Digital Shoreline Analysis System (DSAS) - although is a free extension, is suited for commercial software. Besides, the best free open-source option to calculate EPR called Analyzing Moving Boundaries Using R (AMBUR), since it is a robust and powerful tool, the complexity and heavy processes can restrict the accessibility and simple usage. The validation methodology consists of applying the EPR4Q, DSAS, and AMBUR on different examples of shorelines found in nature, extracted from the U.S. Geological Survey Open-File. The obtained results of each tool were compared with Pearson correlation coefficient. The validation results indicate that the EPR4Q tool created acquired high correlation values with DSAS and AMBUR, reaching a coefficient of 0.98 to 1.00 on linear, extensive, and non-extensive shorelines, guarantying that the EPR4Q tool is ready to be freely used by the academic, scientific, engineering, and coastal managers communities worldwide.

In recent years, rainfall-induced waterlogging has become a common hazard in the highly urbanized coastal city of Chattogram, Bangladesh resulting in high magnitude of property damage and economic loss. Therefore, the primary objective of this research is to prepare a waterlogging inventory map and understand the spatial variation of the risk by means of hazard intensity, exposure, and vulnerability of waterlogging. In this research, the inventory map and factors influencing waterlogging hazard were determined from a participatory survey and other spatial data including land elevation, population, and structural data were collected from secondary sources. Analytical Hierarchy Process was applied to measure the hazard intensity and the exposure and vulnerability were estimated by overlaying the spatial data onto the hazard intensity map. A total of 58 locations in 22 wards have been identified as waterlogging affected, which covers ~8.42% of the city area. Obtained waterlogging vulnerability index map suggests that ward no. 5, 6, 16, 17, and 33 are greatly vulnerable to waterlogging in terms of their social, infrastructure, critical facilities, economic and environmental vulnerability. We show that ~2.71% of the study area is at very high risk, while the risk score is considerably higher for ward no. 5, 8, 17, 19, and 33.

In this study, our goal was to research land-use change by combining spatio–temporal land use/land cover monitoring (LULC (1989–2019) and urban growth modeling (1999–2039) in Benslimane, Morocco, to determine the effect of urban growth on different groups based on cellular automata (CA) and geospatial methods. A further goal was to test the reliability of the AC algorithm for urban expansion modeling. To do this, four years of satellite data were used at the same time as population density, downtown distance, slope, and ground road distance. The LULC satellite reported a rise of 3.8 km2 (318% variation) during 1989–2019. Spatial transformation analysis reveals a good classification similarity ranging from 89% to 91% with the main component analysis (PCA) technique. The statistical accuracy between the satellite scale and the replicated built region of 2019 gave 97.23 %t of the confusion matrix overall accuracy, and the region under the receiver operational characteristics (ROC) curve to 0.94, suggesting the model's high accuracy. Although the constructed area remains low relative to the total area of the municipality's territory, the LULC project shows that the urban area will extend to 5,044 km2 in 2019, principally in the western and southwestern sections. In 2019–2039, urban development is expected to lead to a transformation of the other class (loss of 1,364 km2), followed by vegetation cover (loss of 0.345 km2). In spatial modeling and statistical calculations, the GDAL and NumPy Python 3.8 libraries were successful.

Failaka Island is located 20 kilometers east of the Kuwait mainland. The island includes archaeological sites dating back to the Bronze, Hellenistic, Christian, and Islamic ages. To develop the island as a tourist attraction the state is pursuing a new urban plan based on the island's environmental potential. This study is the basis of the urban plan depends on environmental criteria from the view of Geoarchaeology. The study analysis the land-use and land-cover changes of Failaka Island between 1958- 2018. It provides a topographic survey of the island's coastline and a classification of its geomorphological features and a state of the art identification of its archaeological sites by using a drone to make a terrain model. The study used a medium to high-resolution image analysis of the land-use and land-cover changes: WorldView2-50cm 2010 and 2018; Landsat 8; aerial photography; Drone images and a digital elevation model (DEM), to analysis the expected sea level changes by the end of this century. This study also created a geodatabase of the island that can be adapted for future studies. The results emphasize the importance of preserving the historical and ecological features of the island while developing its infrastructure.

The assessment of rockfall risks on human activities and infrastructure is of great importance. Rock falls pose a significant risk to a) transportation infrastructure b) inhabited areas and c) Cultural Heritage sites. The paper presents a method to assess rockfall susceptibility at national scale in Greece, using a simple rating approach and GIS techniques. An extensive inventory of rockfalls for the entire country was compiled for the period between 1935 and 2019. The rockfall events that were recorded are those, which have mainly occurred as distinct rockfall episodes in natural slopes and have impacted human activities, such as roads, inhabited areas and archaeological sites. Through a detailed analysis of the recorded data, it was possible to define the factors which determine the occurrence of rockfalls. Based on this analysis, the susceptibility zoning against rockfalls at national scale was prepared, using a simple rating approach and GIS techniques. The rockfall susceptibility zoning takes into account the following parameters: (a) the slope gradient, (b) the lithology, (c) the annual rainfall intensity, (d) the earthquake intensity and (e) the active fault presence. Emphasis was given on the study of the earthquake effect as a triggering mechanism of rockfalls. Finally, the temporal and spatial frequency of the recorded events and the impact of rockfalls on infrastructure assets and human activities in Greece were evaluated.

Geospatial Information Systems (GIS) can provide a great environment for using machine learning algorithm for spatial data such as satellite images. Integrating this functionality with artificial intelligence algorithms for analyzing spatial data enables us to predict challenging disasters such as deforestation. Deforestation as an environmental problems has been recorded the most serious threat to environmental diversity and one of the main components of land-use change. In this paper, we investigate spatial distribution of deforestation using artificial neural networks and satellite imagery. We modeled deforestation process using various factors in determining the relationship between deforestation and environmental and socioeconomic factors. Hence, for this purpose, the proximity to roads and habitats, fragmentation of the forest, height from sea level, slope, and soil type are considered in the model. In this research, we modeled land cover changes (forests) to predict deforestation using an artificial neural network due to its significant potential for the development of nonlinear complex models. The procedure involves image registration and error correction, image classification, preparing deforestation maps, determining layers, and designing a multi-layer neural network to predict deforestation. The satellite images for this study are of a region in Hong Kong which are captured from 2012 to 2016. The results of the study demonstrate that neural networks approach for predicting deforestation can be utilized and its outcomes show the areas that destroyed during the research period.

It is widely acknowledged that the integration of BIM and GIS data is a crucial step forward for future 3D city modelling, but most of the research conducted so far has covered only the semantic aspects of GIS-BIM integration. We present here the results of the GeoBIM project, in which we tackled three integration problems focussing instead on aspects involving geometry processing: (i) the automated processing of complex architectural IFC models, (ii) the integration of existing GIS subsoil data in BIM, and (iii) the georeferencing of BIM models for their use in GIS software. All the problems have been studied using real world models and existing datasets made and used by practitioners in the Netherlands. For each problem, we expose in detail the issues we faced, our proposed solutions, and our recommendations for a more successful integration.

Biogenic emissions affect the urban air quality as they are ozone and SOA precursors and should be taken into account when applying photochemical pollution models. The present study presents an estimation of the magnitude of Non-Methane Volatile Organic Compounds emissions (NMVOCs) emitted by vegetation over Greece. The methodology is based on computation performed with the aid of a Geographic Information System (GIS) and theoretical equations in order to develop an emission inventory on a 6x6 spatial resolution, in a temporal resolution of 1hr covering one year (2016). For this purpose, a variety of input data was used: improved satellite land-use data, land-use specific emission potentials, foliar biomass densities, temperature and solar radiation data. Hourly, daily and annual isoprene, monoterpenes and other volatile organic compounds (OVOCs) were estimated. In the area under study, the annual biogenic emissions were estimated up to 472 kt, consisting of 46.6% isoprene, 28% monoterpenes and 25.4% OVOCs. Results delineate an annual cycle with increasing values from March to April, while maximum emissions were observed from May to September, followed by a decrease from October to January.

Historical maps possess significant theoretical and practical value in fields such as historical geography and urban planning, making them essential tools for studying urban spatial structure. This study explores the evolution of Xi'an's urban spatial structure from the Five Dynasties (909 CE) to the late PRC period (1949 CE), using historical maps and Historical GIS (HGIS) methodologies. By integrating Space Syntax and Kernel Density Estimation (KDE) with data from The Historical Atlas of Xi'an, this research identifies key trends in the city's spatial transformation. Two distinct phases of development were observed: the simpler, centralized structure during the Five Dynasties, Northern Song, and Yuan periods, and a more complex, decentralized structure during the Ming, Qing, and PRC periods. These changes were influenced by a dynamic interplay of political, economic, cultural, and environmental factors, as well as the city's need to adapt to external military threats and internal socio-economic shifts. The study emphasizes the value of HGIS in analyzing urban historical geography and the potential it offers for both understanding past urban dynamics and informing contemporary urban planning and cultural heritage preservation efforts. The findings demonstrate how Xi'an's spatial evolution reflects broader trends in Chinese history and provides insights for future sustainable development.

Saudi Arabian cities have experienced a rapid expansion of their urban areas - particularly their city centers - over the last four decades. This has resulted to an increase in vehicular usage, which has adversely affected the daily walking experience of the residents. Walkability has several positive effects on people's health as well as the environment in cities. In addition to being a means of transportation, it also helps people create a sense of place, enhancing their image ability to be easily understood as a legible urban structure, and deepen their emotional bond with the city. Using the medium-sized Saudi Arabian city of Onaizah as a case study, this study looks into the feasibility of creating urban walking corridors to encourage users to walk more. According to Saudi Arabia's Vision 2030, which emphasizes the value of sustainable urban development and raising quality of life for residents, the study addresses walkability as a generator to maintain and improve the urban scene of the city center. Geographic Information Systems (GIS) was used to analyze the data and generate the urban corridors in the city center. The results showed the possibility of improving walking in Onaizah through three urban corridors to the city center, measuring 1,401 meters, 1,283 meters, and 1,507 meters, respectively. These corridors represent urban, commercial and heritage corridors, giving an opportunity for planners and decision makers to challenge prioritize pedestrian connectivity and enhance the physical environment, leading to sustainable urban development. This research paper used various criteria analysis methods to provide a comprehensive assessment of various factors to conclude these urban corridors; for example, assessments of land use, transportation and environmental considerations. The study aligns with Saudi Arabia's vision 2030 by promoting walking and enhancing the city's overall walkability while offering Onaizah society by creating and suggesting urban corridors to the city center a sustainable and livable urban environment.

(1) Background: The rapid expansion of 4G and 5G networks has raised public concerns about potential health risks associated with electromagnetic radiation. This study addresses these fears by investigating electromagnetic measurements in crowded squares, where human exposure is heightened. Despite rigorous scientific methodologies showing generally low levels of radiation from these technologies, public apprehension persists; (2) Methods: We conducted 496 measurements in Larissa's major squares, using a portable field strength analyzer and GIS mapping. Squares were categorized by traffic and population density; (3) Results: Low-traffic squares exhibited exposure ratios consistently below ICNIRP limits, with median values of 0.0012 and 0.00173 in non-active and active mobile phone cases, respectively. In high-traffic squares, the median exposure ratio was 0.00335 in non-active mobile phone cases, remaining below recommended limits; (4) Conclusions: These findings indicate no significant risk of electromagnetic radiation in crowded squares in Larissa, emphasizing the importance of factual communication and dispelling public misconceptions.

In the Lubumbashi charcoal production basin (LCPB) in south-eastern DR Congo, agricultural and charcoal production activities regularly give rise to fires that lead to considerable degradation of the miombo open forest. This study analyzes the drivers of the spatio-temporal distribution of active fires and burnt areas in the LCPB by processing MODIS and Landsat data. In addition, a Kernel density analysis method (KDE) was used to estimate fire risk, while the effect of the road network and dwellings on vegetation fires was highlighted in areas between 0-3000m radius. Obtained results revealed that fires in the LCPB generally occur between April and November mainly during the day, between 11am and 12pm. These fires are concentrated in the central and south-western part of the LCPB, more specifically in the savannahs and near roads. From 2002 to 2022, an average of 11,237 active fires and an average of 6,337 km2 of burnt areas were recorded in the LCPB. Each year, these fires peak in August, and despite their steady decline, the few fires that have affected the forests have caused more devastation (more than 2790 km2 / year) than those observed in the fields and savannah. These figures highlight the imperative need to put in place fire prevention and management measures in the LCPB, with particular emphasis on awareness, monitoring and fire-fighting measures.

The analysis of the land use dynamics of the Lac Télé Community Reserve (RCLT) using Landsat Thematic Mapper (TM) and (Enhance Thematic Mapper) ETM+ images highlight significant changes in the vegetation cover from 1980 to 2000 and 2018. Thus, the rate of forest area decreased by 21.41% for the entire LTCR in favor of savannahs which increased by 15.23%. The conversion of this forest area to savannah due to the practice of slash and burn agriculture facilitates the opening up of the forest area and contributes to greatly degrading the spawning grounds of fish species from the Likouala aux herbes river. For the mapping of fishing activity in general and the ecological characterization of the 151 identified spawning grounds in particular; the respective mean values of the physical and chemical water parameters; temperature (28.13°C), pH (4.23) and depth (3.34) did not vary significantly from one selected village to another between July and September 2019. The fish diversity unregistered during the study, in the 07 pilot villages would be due to the diversity of the microhabitats noted in the villages of the LTCR, especially from the villages; Botongo, Mossengue and Bouanela where the indices of ichthyological diversity were the highest.

In recent decades, particularly intense changes have occurred in Spanish urban areas. This is the result of demographic and urbanizing transformations that have led to a change in the city model. The predominance of compact forms has been replaced by the growing prominence of urban sprawl. Structures are now more extensive, characterized, in turn, by fragmentation and notable consumption of land in the peripheries. In medium-sized cities, the municipalities bordering the central cities have concentrated the processes of suburbanization and periurbanization. This paper addresses the process of change for 34 cities and their urban areas located in inland Spain. A proposal is made for the delimitation and characterization of urban areas, taking the municipality as the unit of analysis. At the methodological level, six variables are used, analyzed by means of multicriteria statistical techniques combined with the use of GIS tools. An Urban Transformation Index (ITU) has been developed that synthesizes urbanizing, demographic and socioeconomic dynamics. In the territorial area, the 20-minute isochrone is used as a reference. In the temporal domain, the period of analysis addresses the events of the first two decades of the twenty-first century.

The Kashmir valley is prone to flooding due to its peculiar geomorphic setup compounded by the rapid anthropogenic land system changes and climate change. The study assesses the impact of land use and land cover (LULC) changes between 1980 and 2020 and extreme rainfall on peak discharge and sediment yield in the Upper Jhelum Basin (UJB), Kashmir Himalaya, India using KINEROS2 model. Analysis of LULC change revealed a notable shift from natural LULC to more intensive human-modified LULC, including a decrease in vegetative cover, deforestation, urbanization, and improper farming practices. The findings revealed a strong influence of the LULC changes on peak discharge, and sediment yield relative to the 2014 timeframe, which coincided with the catastrophic September 2014 flood event. The model predicted a peak discharge of 115101 cubic feet per second (cfs) and a sediment yield of 56.59 tons/ha during the September 2014 flooding, which is very close to the observed peak discharge of 115218 cfs indicating that the model is reliable for discharge prediction. The model predicted a peak discharge of 98965 cfs and a sediment yield of 49.11 tons/ha in 1980, which increased to 118366 cfs and, 58.92 tons/ha respectively in 2020, showing an increase in basin’s flood risk over time. In the future, it is anticipated that the ongoing LULC changes will make flood vulnerability worse, which could lead to another major flooding in the event of an extreme rainfall as predicted under climate change and, in turn compromise achievement of sustainable development goals (SDG). Therefore, regulating LULC in order to modulate various hydrological and land surface processes would ensure stability of runoff and reduction in sediment yield in the UJB, which is critical for achieving many SDGs.

Flash flooding is one of the most devastating natural events that leads to enormous and recurring loss of life. Kuwait was subjected to severe rainstorms in the winter of 2018 and 2020 followed by an extreme violent flood that had not been known in Kuwait since 1976. It resulted in several geomorphological and environmental impacts in urban and desert areas. This produced some positive results, such as geomorphological activity in landforms, the flow of some valleys and the prosperity of wildlife in the Kuwaiti desert. Negative results included some problems in the metropolitan area and destruction of some road networks that intersect the main valleys, and which were not equipped with crossings for avoiding floods. There was also the emergence of some problems in the infrastructure. Study of flash floods requires the involvement of all scientific and executive bodies to avoid environmental risk. The study aims to: 1- Monitor geomorphological and environmental changes. 2- Assess the impact of floods in the urban areas and on infrastructure. 3- Modeling the impact. 4- Creating solutions and adaptions to the flash flood. The study uses several methods such as remote sensing (RS), geographic information systems (GIS), hydrologic modeling and fieldwork to evaluate the impact of flash flood hazards on the sustainable urban development of Kuwait state. This approach is rarely used in Kuwait. We propose a novel method that could help decision-makers and planners in determining inundated flood zones before planning future urban developments in Kuwait, and help them to manage flood water, by identifying the most appropriate places for storage to exploit water in agriculture and drinking.

Choosing the optimal location for a city based on sound environmental geomorphology planning is of the utmost importance to achieving environmental sustainability, as it can spare the State and other decision-making entities a great deal of stress in the long run. GIS offers great potential for environmental planners to choose the most appropriate places for the cities of the future, especially when coupled with environmental geomorphological analyses. The State of Kuwait seeks sustainable development through the implementation of clear and specific urban plans, some of which suffer from a severe lack of geomorphological and spatially based environmental planning. This study aims to: 1) Conduct suitability modelling for establishing new cities in Kuwait, 2) Assess the current 2005-2030 urban plan, and 3) Propose possible recommendations and solutions for potential urban problems. The study relies on integrating several methods to devise a framework that will aid researchers and decision-makers in selecting optimal locations for built structures based on analysis and modelling (e.g., digital elevation model, geologic mapping, geomorphology, natural hazards, heritage/archaeological sites, military areas, oil fields, soils). Using this methodology in choosing city sites contributes to achieving sustainable development, reducing city problems, saving countries’ budgets, and saving lives. Results from this study enhance understanding of how environmental geomorphology, when combined with GIS, can be harnessed to achieve sustainable urban development in the Arabian Gulf countries and other desert countries.

Sea-level rise is a problem increasingly affecting coastal areas worldwide. The existence of Free and Open-Source Models to estimate the sea-level impact can contribute to better coastal man-agement. This study aims to develop and to validate two different models to predict the sea-level rise impact supported by Google Earth Engine (GEE) – a cloud-based platform for planetary-scale environmental data analysis. The first model is a Bathtub Model based on the uncertainty of projections of the Sea-level Rise Impact Module of TerrSet - Geospatial Monitoring and Modeling System software. The validation process performed in the Rio Grande do Sul coastal plain (S Brazil) resulted in correlations from 0.75 to 1.00. The second model uses Bruun Rule formula implemented in GEE and is capable to determine the coastline retreat of a profile through the creation of a simple vector line from topo-bathymetric data. The model shows a very high cor-relation (0.97) with a classical Bruun Rule study performed in Aveiro coast (NW Portugal). The GEE platform seems to be an important tool for coastal management. The models developed have been openly shared, enabling the continuous improvement of the code by the scientific commu-nity.

Sediment accumulation in a dam reservoir is a common happening environmental problem throughout the world. Topographic conditions, land use land cover change, the intensity of rainfall, and the soil characteristics are the major driving factors for sedimentation to occur. The effect of sedimentation in a dam reservoir is very visible in the watershed as a result of hilly topographic conditions, high rainfall intensity, thin land cover, and less soil infiltration capacity. In this paper, an integrated RUSLE and GIS technique was implemented to estimate a mean annual sediment yield based on spatial and temporal variations in Nashe dam reservoir situated in Fincha catchment, Abaya River basin, Ethiopia. Spatial and temporal estimation of mean annual sediment yield was estimated using the Revised Universal Soil Loss Equation (RUSLE) model and GIS. Historical 6-year (2014-2019) rainfall for the temporal variations and other physical factors such as soil erodibility, slope and length steepness, management and land used land cover, and support practice for spatial variations were used as sediment driving factors. The mean annual sediment yield ranges from 0 to 2712.65 t ha-1 year-1 was seen. Spatially, Very high, high, moderate, low, and very low sediment yield severity with total area coverage with 25%, 10%, 30%, 15%, and 20% in 2017, 2015, 2019, 2014, and 2018 respectively. The information about the spatial and temporal variations of the severity of sediment yield in RUSLE model has a paramount role to control the entry of sediment into the dam reservoir in this watershed. The results of the RUSLE model can also be further considered along with the watershed for planning strategies for dam reservoirs in the catchment.

Sea-level rise is a problem increasingly affecting coastal areas worldwide. The existence 15 of Free and Open-Source Models to estimate the sea-level impact can contribute to better coastal 16 management. This study aims to develop and to validate two different models to predict the 17 sea-level rise impact supported by Google Earth Engine (GEE) – a cloud-based platform for plan-18 etary-scale environmental data analysis. The first model is a Bathtub Model based on the uncer-19 tainty of projections of the Sea-level Rise Impact Module of TerrSet - Geospatial Monitoring and 20 Modeling System software. The validation process performed in the Rio Grande do Sul coastal 21 plain (S Brazil) resulted in correlations from 0.75 to 1.00. The second model uses Bruun Rule for-22 mula implemented in GEE and is capable to determine the coastline retreat of a profile through the 23 creation of a simple vector line from topo-bathymetric data. The model shows a very high correla-24 tion (0.97) with a classical Bruun Rule study performed in Aveiro coast (NW Portugal). The GEE 25 platform seems to be an important tool for coastal management. The models developed have been 26 openly shared, enabling the continuous improvement of the code by the scientific community.

The State of Kuwait seeks sustainable development through implementation of clear and specific urban plans, some of which suffer from a severe lack of geomorphological and spatially-based environmental planning, such as the use of geographic information systems (GIS). Choosing the optimal location for a city based on sound environmental planning is of the utmost importance to achieving environmental sustainability, as it can spare the State and other decision-making entities a great deal of stress in the long run. GIS, in particular, offers great potential for the environmental planner and decision-maker to choose the most appropriate places for the cities of the future, especially when coupled with geomorphological analyses. To achieve the Vision of Kuwait 2035, one of the State’s planning objectives focuses on Urban Planning for the Establishment of Environmental Cities that Achieve (UPEECA) environmental sustainability criteria. To gain a more holistic analysis related to the Vision, this study aims to: 1) Conduct suitability modelling for establishing new cities in Kuwait, 2) Assess the current 2005-2030 urban plan, and 3) Propose possible recommendations and solutions for potential urban problems. The study relies on integrating several methods to devise a framework that will aid researchers and decision-makers in selecting optimal locations for built structures based on analysis and modelling (e.g., digital elevation model, geologic mapping, geomorphology, natural hazards, heritage/archaeological sites, military areas, oil fields, soils)

The aim of this paper is to analyze the five grand parks in Dubai, United Arab Emirates (UAE) and provide a geoprocessing approach to different aspects such as sport, health, leisure, recreation, and public wellbeing. The study uses a hybrid of qualitative and quantitative approach as methodology. Sustainability offerings, accessibility for people of determination and special needs, typology and the geolocations of the grand parks plays crucial role in residents’ wellbeing. The paper concludes with recommendations for Dubai government to design new and innovative approaches to manage wellbeing of urban public places into the leisure environment for residents.

This is study follows environmental diversity assessment for geotourism development in Asir region. Geotourism seeks to supporting the tourism landscape in its interaction with the historical and archaeological, architectural or immaterial heritage, and requires diversification in terms of product, market and geographical potential. The study is based on various tourist facades and environmental diversity in Asir. As tourism development is a comprehensive undertaking involving many sectors, and these are the challenges to which the country’s tourism industry should respond to promote domestic tourism. quality and spatial pattern of tourism resources, climate comfort, and natural disaster possibility. Based on analyze multi-source datasets collected, geomorphological features of this area, we created a GIS database comprising geologic and topographic maps, and satellite images using these datasets. The findings of the study provided valuable insights into the role of environmental diversity in achieving tourism. The study examined the interrelationship between tourism and environmental diversity.

Abstract Background: The highest incidence rate of covid-19 in Iran was reported from Shahroud County. This study was conducted by geographic information systems (GIS) to determine the geographical distribution of Covid-19 in 60 days. Study design: A cross-sectional study Methods: This study was conducted in counties covered by Shahroud University of Medical Sciences, namely Shahroud and Mayami, from February 20, 2020 to April 18, 2020. The GIS can better show the spread of epidemics. This software indicates geographical distribution of disease spread and is very helpful in controlling the epidemics. Therefore, maps of spatial distribution and risk of infection to COVID-19 were prepared in different regions of Shahroud county using Arc-GIS software to better implement health policies. Results: During this sixty-day period, 529 confirmed cases were detected, of which 51% were men and the average age was 55 years. The maps showed high-risk to risk-free regions. Shahroud and Bastam cities were known as the coronavirus hot spots. The eastern region of Shahroud has the highest number of cases but some high risk areas are spread throughout the Shahroud city due to high infectivity of virus. Risk-based time maps indicated a reduction in the risk of infection at the end of the research period due to some mitigation and suppression strategies. Conclusions: Shahroud and Bastam are the most dangerous cities that, the number of patients and dissemination has decreased over time because of tracking definite patients and people in contact with them and implementation of preventive care.

Growing potato demands considerable external inputs of pesticides due to its susceptibility to various pests and pathogens. Here we present an attempt to differentiate the Slovak rural landscape with respect to the possibility of effective potato cultivation and to characterise soil parameters of current potato cultivation areas with the aim to increase the sustainability of the potato production. The selection was based on soil climatic, production and economic parameters. By using the GIS tools and existing databases on soil characteristics in Slovakia, maps of soil suitability categories for potato cultivation were generated. In Slovakia, it was found that 12.3% of farmland is very suitable for potato cultivation and that as much as 43.1% is not suitable. Later the specified categories were characterised in detail and specified with respect to geographic, soil, climatic, production and economic parameters. Currently, most potato crops are cultivated on Eutric Cambisols (27%), Chernozems (20%) and Mollic Fluvisols (18%). Loamy soils (65%), soils without gravel (62%), deep soils (74%) and soil situated on plains (55%) are dominant in these regions. We suggest that potato cultivation should be concentrated on the most suitable areas, thereby increasing the economic profitability, improving the ecological stability of the country and supporting the sustainability of the agriculture.

The non-existence of a land ownership database in most of the developing countries moves the inhabitants to the occupation of public lands. Some of this situation are the origin to areas of informal housing, commerce and agriculture and in the end into new ​​informal settlements. Informal settlements become a serious problem in developing countries. The most common typology of informal settlements is that they are the population settled in public lands without any infrastructures and against the administrator's will. Thought this action the result in an uncontrolled land occupation process that promotes new informal areas without any proper built-up utilities, located in risk areas on the territory, barely ensuring the minimum requirements for a heaty living of the population and in various cases incentives to an informal economy. The process of build a cadastral map in informal settlement areas is a fundamental base to support the future transformation of illegal areas and to regulate the occupation of new subdivision planning and into the creation of new expansion areas. In this paper, it is presented a methodology developed to be applied to support a new register of land and to management. The transformation of informal settlement areas. The model to register the land tenure has been associated with allows the process application to multiple typology of informal settlements. The model to register land tenure has developed on a series of qualitative and quantitative data that determine the identification and classification of the buildings and its physical and functional description. The model was developed using Geographic Information System and with an initial survey of existing land titles of possession and public proposals to develop new expansion areas. A case study of the method is presented, where the land management model was implemented - Chã da Caldeiras in Ilha do Fogo an informal settlement in Cape Verde. The results are a great acceptance of the proposal by the population and local authorities and the starting of the implementation phase.

Over the years, the Egyptian temples at Luxor city have been intensely investigated, but most of these studies just focused on the classical sides of the archaeological and historical descriptions. Many of the environmental problems are inevitable results of the unplanned urban crawling around the monuments temples. This paper aims at assessing the environmental changes around some temples of Luxor City using Remote sensing and GIS techniques. In particular, a historical database made up of Corona and Landsat TM data have been investigated along with the new acquisitions of Quickbird2 and Sentinel2. Results from our investigation highlighted rapid changes in urban and agricultural areas, which adversely affected the Egyptian monumental temples causing serious degradation phenomena. Using the information obtained from our RS&GIS based analysis, mitigation strategies have been also identified for supporting the preservation of the archaeological area.

This study uses Historical Geographic Information Systemsand spatial analysis methods like Space Syntax and Kernel Density Estimation to examine the urban spatial evolution of Xi'an from the Five Dynasties (907 CE) to the early People's Republic of China (1949 CE). By focusing on six historical periods, it reveals the city's transition from a centralized layout to a decentralized, multi-center form. The research shows that Xi'an's spatial structure was shaped by political, economic, cultural, and environmental forces. The Ming and Qing dynasties, in particular, saw economic diversification and the influence of the Silk Road, driving the city's outward expansion and the formation of multiple functional zones. The study also discusses how these historical changes offer valuable lessons for modern urban planning, particularly in balancing cultural heritage preservation with modernization. Overall, the findings provide fresh insights into the long-term urban development of historic cities and offer practical strategies for modern urban planning.

A typical orchard's mechanical operation consists of three or four stages: lining, digging for plantation, moving the seedling from nurseries to the farm, moving the seedling to the planting hole, and planting the seedling in the hole. However, the digging the planting hole is the most time-consuming operation. In fruit orchards, robots' usage is increasingly becoming more prevalent to increase operational efficiency. They offer practical and effective services to both industry and people, whether they are assigned to plant trees, reduce the use of chemical fertilizers, or carry heavy loads to relieve staff. Robots can operate for extended periods of time and can highly adept at repetitive tasks like planting many trees. The present study aims to identify the locations for planting trees in orchards using geographic information systems (GIS), to develop an autonomous drilling machine and use the developed robot to open planting holes. There isn't a comparable study on autonomous hole planting in the literature in this regard. The agricultural mobile robot is a four wheeled nonholonomic robot with differential steering and forwarding capability to stable target positions. The designed mobile robot can be used in fully autonomous, partially autonomous, or fully manual modes. The drilling system, which is a y-axis shifter driven by a DC motor with reducer includes an auger with a 2.1 HP gasoline engine. SOLIDWORKS software was used for designing and drawing of the mobile robot and drilling system. The Microsoft Visual Basic.NET programming language was used to create the robot navigation system and drilling mechanism software. The cross-track error (XTE), which determines the distances between the actual and desired holes positions, was utilized to analyze the steering accuracy of the mobile robot to the drilling spots. Consequently, the average of the arithmetic means was determined to be 4.35 cm, and the standard deviation was 1.73 cm. This figure indicates that the suggested system is effective for drilling plant holes in orchards.

Flooding, exacerbated by climate change, poses a significant threat to certain areas, increasing in frequency and severity. In response, the construction of supplementary dams has emerged as a reliable solution for flood management. This study employs a Remote Sensing (RS) approach integrated with GIS to assess the feasibility of constructing a supplementary dam near Linville, Brisbane, Australia, with the aim of mit-igating floods and preventing overtopping failure at the Wivenhoe Dam. Using QGIS software and a 25-meter resolution DEM from the Queensland Spatial Catalogue ‘QSpatial’ website, four potential dam sites were analysed, considering cross-sections, watershed characteristics, and water volume calculations. Systematic selection criteria were applied to identify the most optimal option based on dam wall di-mensions, volume-to-area, and volume-to-cost ratios. The selected option was further assessed against predefined criteria, yielding one optimal choice. The study provides insights into the feasibility and ef-fectiveness of supplementary dam construction for flood mitigation in the region, with recommendations for future research and implementation.

.(1) Background: Groundwater resources management in dry lands, characterized by climate var-iability and population growth, is difficult. Exploration and exploitation of groundwater, due to inadequate surface water is very costly. The present study employed Analytic Hierarchy Process (AHP) and GIS to identify Ground-Water Potential (GWP) areas in a semi-arid region of Nigeria; (2) Methods: Land-use-land-cover, drainage density, slope, rainfall, static water level, soil media, vadose media and aquifer media were selected for GWP analysis. Parameters weights were de-termined using AHP and ranked based on their contribution to GWP by experts. The parameters were then integrated using weighted overlay tool in ArcGIS 10.5 to produce GWP map of the study area. Boreholes yield data from 245 wells were collected to determine the model accuracy and model validation; (3) Results: Results classified the study area into very high GWP (1.9%), high GWP (8.8%), moderate GWP (62%), low GWP (20.70%) and very low GWP (6.6%). Validation of the AHP model with boreholes yield data shows a correlation coefficient of 71.3% giving a good prediction; (4) Conclusions: AHP and GIS can be used to successfully map GWP areas which could serve as an exploration guide for sustainable management of groundwater resources in semi-arid areas.

The development of storage standards for databases of different nature and origin makes it possible to aggregate and interact with different data sources in order to obtain and show complex and thematic information to the end user. This article aims to analyze some possibilities opened up by new applications and hypothesize their possible developments. With this work, using the currently available Web technologies, we would like to verify the potential of the use of Linked Open Data in the world of WebGIS and illustrate an application that allows the user to interact with Linked Open Data through their representation on a map. Italy has a artistic and cultural heritage unique in the world and the italian Ministry of Cultural Heritage and Activities and Tourism has created and made freely available a dataset in Linked Open Data format that represents it. With the aim of enhancing and making this heritage more usable, the National Research Council (CNR) has created an application that presents this heritage via WebGIS on a map. Following criteria definable by the user, such as the duration, the subject of interest and the style of the trip, tourist itineraries are created through the places that host this heritage. New possibilities open up where the tools made available by the Web can be used together, according to pre-established sequences, to create completely new applications. This can be compared to the use of words, all known in themselves, which, according to pre-established sequences, allow us to create ever new texts.

Peri-urban forests often have extensive networks of greenways that allow for outdoor recreation. However, information associated with these greenways often does not include their degree of naturalness, which is usually reduced to descriptions of flora and fauna and often overlook the factors that reduce naturalness. Therefore, in some cases, the naturalness of these greenways is not as high as expected. Having a quantification of their naturalness would be useful, especially for hikers interested in appreciating and enjoying nature. Additionally, this information would help outdoor recreation managers to design trails or decide which ones to promote as "greenways". The objectives of this study are (1) to design a method to calculate and map the naturalness of greenways using two approaches, one based on perceptual fieldwork and the other on Geographic Information Systems (GIS); (2) to apply the designed method to a specific greenway; and (3) to compare both methodological approaches. The results show that, for the greenway studied, the naturalness scores obtained are low in all three types of analyses used. Around 70% of the greenway sections in the GIS visibility analysis and 80% in the GIS proximity analysis have a low naturalness index, while this value is reduced to 40% with the fieldwork analysis. Although the results of the GIS approach (proximity and visibility) generate naturalness indices and spatial patterns that are very similar, they differ significantly from those derived from the fieldwork analysis. The discussion of the results suggests that the three methodologies used are valid for analysing the degree of naturalness of the trails, but if used together, it would be possible to add flexibility to the type of variables incorporated in the analysis.

Land degradation and climate change are among the main threats to the sustainability of ecosystems worldwide. Therefore, the restoration of degraded landscapes is essential to maintain the functionality of ecosystems, especially those with greater social, economic and environmental vulnerability. Nevertheless, policy-makers are frequently challenged by deciding on where to prioritize restoration actions, which usually includes to deal with multiple and complex needs under an always short budget. If these decisions are not taken based on proper data and processes, restoration implementation can easily fail. To help decision-makers taking informed decisions on where to implement restoration activities, we have developed a semiautomatic geospatial platform to prioritize areas for restoration activities based on ecological, social and economic variables. This platform takes advantage of the potential to integrate R coding, Google Earth Engine cloud computing and GIS visualization services to generate an interactive geospatial decision-maker tool for restoration. Here, we present a prototype version called "RePlant alpha" which was tested with data from the Central Zone of Chile. This exercise proved that integrating R and GEE was feasible, and that the analysis, with at least six indicators and for a specific region was also feasible to implement even from a personal computer. Therefore, the use of a virtual machine in the cloud with a large number of indicators over large areas is both possible and practical.

Abstract Geographic Information System and Remote Sensing played an important role in analyzing environmental and socio-economic drivers that created favorable condition for malaria breeding as well as in identifying hazard and risk areas. This study gives great emphasis on mapping malaria hazard and risk areas in Gedio zone of SNNPs using geospatial technology. The study identifies two major drivers like Environmental (physical) factors: which provide for the endurance of mosquitoes and Socio-economic factors. The above data were presented and analyzed quantitatively. The content analysis shows that Malaria hazard prevalence areas were mapped based on the environmental factors which are potential of providing good environmental conditions for mosquito breeding. The hazard map was produced using elevation, slope, proximity to breeding sites, and soil type as the factors for breeding mosquitoes. The malaria hazard analysis of the Gedio zone revealed that from the total area, 9.83%, 35.29% is mapped as a very high and high-risk area, whereas, the remaining 38.73%, a 16.14%, and 0.01% were mapped as moderate, low, very low level of malaria hazard respectively. The total area of the study area more than 1/3rd of the area is identified as a very high and high malaria risk area while the rest 2/3rd of an area is considered as a moderate to very low hazard risk zone. Accordingly, very high malaria risk area is found around towns because of population density. Finally, I recommend that the concerned body should have to expand health center, creating awareness of society, especially around populated areas where the risk is high and environmental and individual sanitation can reduce the risk of malaria.

Research is still lacking regarding the question as to how programs to promote healthy aging should be organized in order to increase acceptance and thus effectiveness. For older adults, ecological factors, such as physical distance to program sites, might predict participation and retention. Thus, the key aim of this analysis was to examine these factors in a physical activity intervention trial. Adults (N=8,299) aged 65 to 75 years were invited to participate and n=589 participants were randomly assigned to one of two intervention groups with 10 weeks of physical activity home practice and exercise classes or a wait-list control group. Response, participation, and dropout data were compared regarding ecological, individual, and study-related variables. Kaplan-Meier curves and Cox regression models were used to determine predictors of dropout. In total, 405 participants completed the study. Weekly class attendance rates were examined regarding significant weather conditions and holiday periods. The highest rates of nonresponse were observed in districts with very high neighborhood levels of socioeconomic status. In this study, ecological factors did not appear to be significant predictors of dropout, whereas certain individual and study-related variables were predictive. Future studies should consider these factors during program planning to mobilize and keep subjects in the program.

The accurate assessment and monitoring of soil erosion is of great significance for guiding food production and ensuring ecological security, and it is a current research hotspot. In this paper, remote sensing and geographic information systems (GISs) are combined with the Revised Universal Soil Loss Equation (RUSLE model) to carry out research on soil erosion monitoring and make a quantitative evaluation. According to five factors, including rainfall erosivity, soil erodibility, topography, vegetation cover, crop management and water and soil conservation measures, the distribution of the soil erosion rate in Jilin Province in 2019 was mapped, and the soil erosion rate was divided into 5 levels according to the degree of erosion, including very slight, slight, moderate, severe and extremely severe erosion. Based on the segmented S-slope factor model and the unique topographical features of the study area, the relationships among the soil erosion rate, erosion risk level, erosion area, erosion amount and slope angle (θ) were systematically analysed, and a slope angle of 15° was identified as the threshold for soil erosion on sloped farmland in Jilin Province. The total soil erosion in Jilin Province was 402.14×106 t in 2019, the average soil erosion rate was 21.6 t·ha-1·a-1, and the average soil loss thickness was 1.6 mm·a-1; these values were far greater than the soil erosion rate risk threshold of 10 t ·Ha-1·a-1. Thus, the province has a strong level of soil erosion. We conclude that soil degradation is accelerating, and food production and the ecological environment will face severe challenges. It is suggested that soil erosion control should be carried out according to different types and slopes of land, with an emphasis on the management of forestland and farmland because forestland and farmland are currently the first types of land to be managed in Jilin Province. This paper aims to explore a timely, fast, efficient and convenient soil erosion monitoring and evaluation method and provide effective monitoring tools for agricultural water and soil conservation, ecological safety management and stable food production in Jilin Province and similar black soil areas.

Merchant marine fleet is under inspections by several parties to ensure maritime regulation compliance. One of the major effects on implementation of regulations by International Maritime Organization (IMO) is indeed Port State Control. This article aims to analyze Paris Memoranda of Understanding (MOU) all detention remarks from 2013 to 2019 for EU15 countries (except Luxemburg and Austria) through an approach based on Analytical Hierarchy Process and demonstrate the results on Geographic Information System (GIS) to guide marine industry on detainable Port State Control remarks and country risk profile. While Analytical Hierarchy Process Approach has been used to indicate the ranking of basic maritime regulations from the perspective of the port state control , GIS help us to demonstrate the regional dispersion amongst EU15. The data of the detained vessel taken from the public website of Paris MOU and each report considered as a professional judgement that causes detention. It has been shown that almost all countries top priorities for regulation are Safety of Life at Sea (SOLAS) and Fire Safety Systems (FSS). Consequently, the results of the study can assist Port State Officers, ship crew, ship owners and managers presenting the facts of their inspection and able to improve themselves. The spatial analysis also expected to guide ship owners and managers to focus their vessel’s deficiencies to prevent sub-standardization.

The city of Barcelona, ​​like other cities in the world, suffers strong internal socio-economic inequalities. Numerous works have sought to detect, quantify, characterize and / or map existing intra-urban differences, almost always based on quantitative methodologies. With this contribution, we intend to illuminate the complementary role that qualitative methodologies can play in studies on urban socio-environmental vulnerability. We consider aspects that are not quantifiable but that may be inherent to many such vulnerable spaces, both in the constructed environment and in the social ambit. These questions are considered through selected neighborhoods of Barcelona which have been shown (in prior works, mainly studies of quantitative manufacturing) to possess elements of vulnerability including a high presence of immigrants from less-developed countries, low per capita income, aging populations, or low educational levels. The results reveal the multidimensionality of vulnerability in the neighborhoods analyzed, as well as the essential complementarity among methodologies that detect and support possible public actions aimed at reducing or eliminating intra-urban inequalities.

Green Infrastructure (GI) is increasingly prioritised in landscape policy and planning, due to its potential to benefit ecosystems and enhance wildlife conservation. However, due to the uneven distribution of protected areas (PAs) and the fragmentation of habitats more generally, multi-level policy strategies are needed to create an integrated GI network bridging national, regional and local scales. To help address this challenge, we present proposals to integrate GI elements for the province of Almeria, southeast Spain. We delimit and define key GI elements, and apply GIS-based connectivity tools to generate ecological corridors connecting the province’s 35 PAs. Currently, the PAs are threatened by the advance of intensive greenhouse agriculture and tourism-related urban growth of the municipalities to which they belong. Common patterns in the location of the most suitable areas for the development of GI can be identified. New GI networks would be located along the coast, specifically in the western part of the study area. They would also occupy the valleys of the main rivers of the province. Around 50% of the area occupied by the proposed corridors would be located in places of medium and high suitability for the movement of species between core areas.

Abstract Introduction PCa is one of the cancers that exhibits the widest disparity gaps. Geographical place of residence has been shown to be associated with healthcare access/utilization and PCa outcomes. Geographical Information Systems (GIS) are widely being utilized for PCa disparities research, however, inconsistencies in their application exist. This systematic review will summarize GIS application within PCa disparities research, highlight gaps in the literature and propose alternative approaches. Methods This paper followed the methods of the Cochrane Collaboration and the criteria set of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Articles published in peer-reviewed journals were searched through the PubMed, Embase, and Web of Science databases till December 2022. The main inclusion criteria were employing a GIS approach and examining a relationship between geographical components and PCa disparities. The main exclusion criteria were studies conducted outside the US and those that were not published in English. Results A total of 25 articles were included, 23 focused on PCa measures as outcomes: incidence, survival, and mortality while only two examined PCa management. GIS application in PCa disparities research was grouped into three main categories: mapping, processing, and analysis. GIS mapping allowed for the visualization of quantitative, qualitative, and temporal trends of PCa factors. GIS processing was mainly used for geocoding and smoothing of PCa rates. GIS analysis mainly served to evaluate global spatial autocorrelation and distribution of PCa cases, while local cluster identification techniques were mainly employed to identify locations with poorer PCa outcomes, soliciting public health interventions. Discussion Varied GIS applications and methodologies have been used in researching PCa disparities. Multiple geographical scales were adopted leading to variation in associations and outcomes. Geocoding quality varied considerably leading to less robust findings. Limitations in cluster-detection approaches were identified especially when variations were captured using the Spatial Scan Statistic. GIS approaches utilized in other diseases might be applied within PCa disparities research for more accurate inferences. A novel approach for GIS research in PCa disparities could be focusing more on geospatial disparities in procedure utilization especially when it comes to PCa screening techniques. Conclusion This systematic review summarized and described the current state and trend of GIS application in PCa disparities research. Although GIS is of crucial importance when it comes to PCa disparities research, future studies should rely on more robust GIS techniques, carefully select the geographical scale studied, and partner with GIS scientists for more accurate inferences. Such interdisciplinary approaches have the potential to bridge the gaps between GIS and cancer prevention and control to further advance cancer equity.

This paper introduces an intelligent model that combines military expertise with the latest ad-vancements in machine learning (ML) and Geographic Information Systems (GIS) to support humanitarian demining decision-making processes. The model is based on direct input and val-idation from field decision-makers for their practical applicability and effectiveness. With a survey polling the inputs of demining experts, 95% of the responses came with an affirmation of the potential of the model to reduce threats and increase operational efficiency. It includes mili-tary-specific factors that factor in the proximity to strategic locations as well as environmental variables like vegetation cover and terrain resolution. With Gradient Boosting algorithms such as XGBoost and LightGBM, the accuracy rate is almost 97%. Such precision levels further enhance threat assessment, better allocation of resources, and around a 30% reduction in the cost and time of conducting demining operations, signifying a strong synergy of human expertise with algo-rithmic precision for maximal safety and effectiveness in demining

The rehabilitation of abandoned mining sites is an increasingly pressing issue in the context of sustainable development. Recent research has emphasized the need for a holistic approach to the abandoned mining sites and their environmental rehabilitation. Based on field analysis, environmental assessments, satellite imagery processing and geographic information operations, this paper pushes forward the existing knowledge by doing a comprehensive assessment of abandoned mining sites in the Romanian Carpathians and by proposing innovative and sustainable rehabilitation solutions. Our findings highlight that abandoned mining sites and their surrounding territories in the Romanian mountains have significant ecological imbalances and complex socio-economic issues. The findings also suggest that by adopting innovative, integrated, and sustainability-oriented approaches, territories affected by mining can be transformed into valuable and sustainable spaces to meet human needs. We conclude by presenting the importance of innovation in ecological reconstruction and spatial-functional reintegration of mining sites in mountain areas as a useful tool in making fair decisions, both in the context of implementing appropriate development policies as well as for resilience and environmental sustainability of mining-affected mountain areas.

This article presents the suitability analysis of onshore wind farm sites using an AHP (Hierarchical Analysis Process) multi-criteria decision-making (MCDM) approach based on geographic information system modeling (GIS). This analysis is based on different technical aspects (wind speed, elevation, slope), economic (proximity to the electricity network, proximity to roads) and social (proximity to residential areas, population density). The introduction of the factor “population density” and the constraint “required area” rarely used in previous studies makes it possible to refine the results. The data is classified into several categories in a gradual manner, namely unsuitable (0); less suitable (1); suitable (2); highly suitable (3) and most suitable (4). The results of the study show us that the largest percentage relating to spatial coverage is observed in the “suitable” range with 50.89% and the corresponding TWPP (theoretical wind power potential) is 1377.926 GW; it is followed by “highly suitable” 5.67% and a TWPP of 153.652 GW; then “less suitable” with 1.04% and a TWPP of 28.163GW. No areas were identified in “most suitable” and finally the “unsuitable” range with 42.4%. The results show that the most favorable areas are located in the part of the far north of the country corresponding to the strong wind zone. The sensitivity analysis was carried out based on scenario tests (technical, economic and equal-weight) in order to give greater visibility of choice to the different stakeholders who would like to invest in onshore wind energy in Cameroon. This study is useful in more than one way because the results obtained can help investors, the government and other stakeholders to identify potential areas for the deployment of wind energy systems in Cameroon. Furthermore, a study of hybrid solar-wind systems could improve the efficiency of electricity parks in Cameroon.

The anthropogenic deterioration of aquatic ecosystems affects water resources due to agricultural malpractices, pollution from domestic septic tanks, recreational activities, and poor watershed management, among others. This study examines the management of Las Curias Reservoir (176,974 m2), San Juan, Puerto Rico, post the 2016 arrival of the invasive aquatic fern Salvinia molesta. In December 2019, a community-led initiative introduced the Cyrtobagous salviniae weevil, an effective biological control agent for S. molesta, and commenced a mechanical removal campaign using an aquatic harvester. Limnological sampling (September 2019 to March 2022) and drone flights were employed to measure physicochemical and floating plant cover changes, respectively, in the reservoir. Monitoring of weevils in the reservoir demonstrated a rapid establishment and dispersal, which resulted in visible damage including browning of plants and eventually sinking of entire mats. By January 29, 2021, 71% (125,652 m2) of the reservoir surface was covered by salvinia. In 2022, the reservoir recorded an average dissolved oxygen concentration of 2.38 mg/L -1 (± 0.021, n = 144), the highest in the study period and indicative of ecosystem recovery. After three years of control efforts, dissolved oxygen, pH, and specific conductance returned to levels recorded prior to Salvinia molesta introduction. This ecosystem recovery, a first in Puerto Rico, could be attributed to an early use of mechanical control and a long-term impact of biological control.

The construction of sustainable road and highway networks in the world, despite numerous feasibility, pre-feasibility and execution studies, are always confronted with the demands and vagaries of foreseeable and unforeseeable natural disasters. Studying cyclones, earthquakes, fracturing and landslide zones along roads is therefore a challenge for the sustainability of these infrastructures. In many countries around the world, the methods generally used for these studies are not only expensive and time-consuming, but the results obtained are not always efficient. This work examines whether Landsat 8 (with a high cloud level) and SRTM data can be used in both equatorial and coastal Central Africa zones to produce relevant mapping, locating fracture and landslide zones, in order to contribute not only to better road layout at lower cost and in a relatively short time, but also to better prevention of geological disasters that may occur on its network. To this end, a map of the main road network was produced and validated with field data, as well as the maps of the main unstable slopes, faults and fractures zones intersecting the road or highway network. These approaches are useful for sustainable planning, management, monitoring and extension of roads worldwide especially, in Central Africa.

Background. Green spaces contribute to a significant life quality and maintain the sustainability of cities. In Benin, despite the political greening willingness, municipalities are experiencing technical issues in finding suitable spaces to achieve this goal. This study should serve many other towns. It aims at identifying suitable areas for green spaces to integrate landscaping into urban planning in Abomey-Calavi district. Methods. The multi-criteria analysis combining GIS and the hierarchical classification approach were performed. Six factors (land use, altitude, slope, distance from main roads, proximity to urban centres and distance from flood zones, water bodies) were combined using the ArcGIS "Spatial Analysis" extension to generate a map of green space suitability. Results. large areas of land, of which 23.27% are very suitable and 26.06% are suitable for landscaping in this municipality. The ranking of the factors revealed altitude, proximity to road networks, large conurbations and distance from wetlands accounting for 18%, compared with 14% for the other factors regarding the study environment. However, the use of these results must take into account the existing inhabited areas for a good site selection. Conclusion. These outputs guidance to decision-makers in choosing suitable sites for green spaces there and integrating them into sustainable development.

The province of Jaén (Andalusia, Spain), despite being declared the European territory with the largest number of defensive constructions (castles and fortifications), has few conservation plans, with many remains included on the Red List of Spanish Heritage lying abandoned. This presents a problem for the conservation of the landscape and the optimal use of the province’s tourism potential. Through the study case of Magaña hamlet two actions are proposed to alleviate this situation: The creation of an archaeological and environmental risk chart with which to answer such questions as “How have climate change, anthropic alterations and environmental charac-teristics affected the state of conservation of certain heritage sites?” and to put forward proposals for improving their protection using as a basis digital and technological tools, such as remote sensing (SAR), taking advantage of data from the Sentinel 2A and 2B satellites, HBIM, RPAS and GIS. To foster the promotion of smart tourism by digitalising and virtualising tourist routes and archaeological remains by building a Smart Tourism App for mobile devices. Finally, public administrations will be apprised of the need to implement a conservation policy for cultural as-sets and their surroundings in a simple, quick and cost-effective manner.

The current study was planned to simulate runoff due to the snowmelt in the Lidder River catchment of Himalayan region under climate change scenarios. A basic degree-day model, Snowmelt-Runoff Model (SRM) was utilized to assess the hydrological consequences of change in climate. The SRM model performance during the calibration and validation was assessed using volume difference (Dv) and coefficient of determination (R2). The Dv was found as 11.7, -10.1, -11.8, 1.96, and 8.6 during 2009-2014, respectively, while the R2 is 0.96, 0.92, 0.95, 0.90, and 0.94, respectively. The Dv and R2 values indicating that the simulated snowmelt runoff has a close agreement with the observed value. The simulated findings were also assessed under the different scenarios of climate change: a) increases in precipitation by +20 %, b) temperature rise of +2 °C, and c) temperature rise of +2 °C with a 20 % increase in snow cover. In scenario "b", the simulated results showed that runoff increased by 53 % in summer (April–September). In contrast, the projected increased discharge for scenarios "a" and "c" was 37 % and 67 %, respectively. In high elevation data-scarce mountain environments, the SRM is efficient in forecasting future water supplies due to the snowmelt runoff.

Growing interest is awarded to the digitalization of the building permitting use case and many works are developed about the topic. However, the subject is very complex and many aspects are usually tackled separately, making it very hard for traditional literature reviews to grasp the actual progress in the overall topic. This paper unveils the detailed state of the art in Digital Building Permitting (DBP) by critically analyzing the literature by means of a set of coding tags (research progress, implementation, affected DBP workflow steps, ambitions addressed) assigned by a multidisciplinary team. The mainly addressed aspects of the digitalization of building permits resulted to be the technologies to check the compliance of design proposals against regulations, followed by the digitalization of regulations. Lacking aspects are instead the involvement of officers, scalability of solutions and interoperability of data, intended both as data validation and as integration of geoinformation with building models.

River valley bottoms have hydrological, geomorphological, and ecological importance and are buffers for protecting the river from upland nutrient loading coming from agriculture and other sources. They are relatively flat, low-lying areas of the terrain that are adjacent to the river and bound by increasing slopes at the transition to the uplands. These areas have under natural conditions, a groundwater table close to the soil surface. The objective of this paper is to present a stepwise GIS approach for the delineation of river valley bottom within drainage basins and use it to perform a national delineation. We developed a tool that applies a concept called cost distance accumulation with spatial data inputs consisting a river network and slope derived from a digital elevation model. We then used wetlands adjacent to rivers as a guide finding the river valley bottom boundary from the cost distance accumulation. We present results from our tool for the whole country of Denmark carrying out a validation within three selected areas. The results reveal that the tool visually performs well and delineates both confined and unconfined river valleys within the same drainage basin. We use the most common forms of wetlands (meadow and marsh) in Denmark's river valleys known as Groundwater Dependent Ecosystems (GDE) to validate our river valley bottom delineated areas. Our delineation picks about half to two-thirds of these GDE. However, we expected this since farmers have reclaimed Denmark's low-lying areas during the last 200 years before the first map of GDE was created. Our tool can be used as a management tool, since it can delineate an area that has been the focus of management actions to protect waterways from upland nutrient pollution.

COVID-19 pandemic event requires a rapid response from various organizations at the international and national levels. One important response is the provision of information sharing facilities and monitoring of the spread of cases around the world, JHU CSSE developed the Dashboard in January 2020 and followed by WHO the same month for the WHO COVID-19 dashboard. Both dashboards have distributed information as expected by the user with their respective pros and cons. JHU CSSE Dashboard provides faster information with good access to mobile device users even though the display and color selection are less attractive. Information on the WHO COVID-19 Dashboard is often late but more data appearances and variations and comparisons between countries can be made. In the Indonesian context, ESSC for COVID-19 Geoportal as Esri Indonesia initiative has been developed with the support of data and information from various parties and developed with the principles of big data management which are fully supported by adequate spatial portal developer software from Esri. Particularly in Indonesia, there is not yet an adequate system to support spatial based decision making at the local level, therefore the development of a GIS dashboard to support provincial and district governments is highly recommended.

The authors introduce the GRASS GIS add-on module g.citation as an initial implementation of a fine-grained software citation concept. The module extends the existing citation capabilities of GRASS GIS, which until now only provide for automated citation of the software project as a whole, authored by the GRASS Development Team, without reference to individual persons. The functionalities of the new module enable individual code citation for each of the over 500 implemented functionalities, including add-on modules. Three different classes of citation output are provided in a variety human- and machine-readable formats. The implications of this reference implementation of scientific software citation for both for the GRASS GIS project and the OSGeo foundation are outlined.

Dogs are considered property under UK law, while current discourses of pet ownership place canine companions as part of an extended family. This means sentences for those who steal dogs are not reflective of a dogs’ sentience and agency, rather reflecting the same charges for those who steal a laptop or wallet. This is particularly problematic as dog theft is currently on the rise in England and Wales and led to public calls to change the law. Recognizing that a more robust analysis of dog theft crime statistics is required, we gathered dog theft data for 2015, 2016 and 2017 from 37 of 44 police forces through FOI requests. This paper uses this data to examine how dog theft crime statistics are constructed; assesses the strengths and weaknesses of this data; and categorizes, maps and measures dog theft changes temporally per police force in England and Wales. Our findings reveal there has been an increase in dog theft crimes, 1,294 in 2015, 1,525 in 2016 (+17.85%), and 1,678 in 2017 (+10.03%); and a decrease in court charges related to dog theft crimes, 62 (4.7%) in 2015, 48 (3.14%) in 2016, 37 (2.2%) in 2017. There were police force inconsistencies in recording dog theft crime which meant some data was unusable or could not be accessed or analysed. There is a need for a qualitative study to understand dog theft crime in different areas, and standardised approach to recording the theft of a dog by all forces across England and Wales.

On the occasion of Kynouria and in order to achieve the protection and projection of antiquities, a web-based model is proposed for highlighting individual monuments and archaeological sites, having in mind the historical and archaeological evidence of the region, the topography, the demographic profile and the tourist infrastructure, and combining them with the development programs for cultural routes. Therefore, creating suitable databases and mapping the monuments in the area are key prerequisites of the process, as they contribute to an objective assessment of the current situation and then to make rational decisions. In this frame, modern technology provides some important planning tools (GIS, GPS, and OMS) which allow recording and mapping of data, viewing the relationships between them in the area where they appear and managing their projection. The complete study of Kynouria’s archaeological routes contains the implementation of a website using free or open-source software, which should include all the necessary procedures and the historical and archaeological information material (text, maps, and photographs).

Satellite imagery has had limited application in the analysis of pre-colonial settlement archaeology in the Caribbean; visible evidence of wooden structures perishes quickly in tropical climates. Only slight topographic modifications remain, typically associated with middens. Nonetheless, surface scatters, as well as the soil characteristics they produce, can serve as quantifiable indicators of an archaeological site, which can be detected by analysis of remote sensing imagery. A variety of data sets were investigated, with the intention to combine multispectral bands to feed a direct detection algorithm, providing a semi-automatic process to cross-correlate the datasets. Sampling was done using locations of known sites, as well as areas with no archaeological evidence. The pre-processed very diverse remote sensing data sets have gone through a process of image registration. The algorithm was applied in the northwestern Dominican Republic on areas that included different types of environments, chosen for having sufficient imagery coverage, and a representative number of known locations of indigenous sites. The resulting maps present quantifiable statistical results of locations with similar pixel value combinations as the identified sites, indicating higher probability of archaeological evidence. The results show the variable potential of this method in diverse environments.

This study proposes a novel hybrid machine learning approach for modeling of rainfall-induced shallow landslides. The proposed approach is a combination of an instance-based learning algorithm (k-NN) and Rotation Forest (RF), state of the art machine techniques that have seldom explored for landslide modeling. The Lang Son city area (Vietnam) is selected as a case study. For this purpose, a spatial database for the study area was constructed, and then, was used to build and evaluate the hybrid model. Performance of the model was assessed using Receiver Operating Characteristic (ROC), area under the ROC curve (AUC), success rate and prediction rate, and several statistical evaluation metrics. The results showed that the model has high performance with both the training data (AUC = 0.948) and the validation data (AUC = 0.848). The results were compared with those obtained from soft computing techniques i.e. Random Forest, J48 Decision Trees, and Multilayer Perceptron Neural Networks. Overall, the performance of the proposed model is better than those obtained from the above methods. Therefore, the proposed model is a promising tool for landslide modeling. The research result can be highly useful for land use planning and management in landslide prone areas.

The European Commission has been striving to achieve sustainable development in its rural areas for more than 25 years through funds aimed at modernizing the agricultural and forestry sectors, protecting the environment and improving the quality of life. But is sustainable rural development really being accomplished? This study sets out to answer this question in the case of Extremadura, a Spanish territory with Low Demographic Density and a Gross Domestic Product still below 75 % of the European average. Both qualitative and quantitative methodology have been employed, using a Principal Component Analysis the result of which has provided us with a model which shows how various behaviors coexist in the region in view of the distribution of current funding from the EAFRD. The most dynamic areas have received the largest amounts of funding and these are linked to the agricultural sector and to the protection of the environment, leaving aside the more depressed areas and the implementation of the LEADER Approach as well. Therefore, we have come to the conclusion that the current rural development in Extremadura is not sustainable enough.

In this study the transboundary transport of air masses over the water table of Lake Baikal during the raining period of July–August 2010–2019 was investigated. For this purpose, the backward trajectories with an interval of 6 hours were constructed by using the NOAA hysplit method. These trajectories started at the center of Lake Baikal at altitudes of 250 and 2500 m. It was shown that during these years the western air mass transport passed over the marshland of Western Siberia, as well as over the steppe and forest-steppe regions of south of Western Siberia, and the angular sectors of WNW (West - North West) and WSW (West - South West) directions, accordingly, dominated. In July, at altitudes of 2500 m, in average 23.68 % percents of the total amount of trajectories have passed over the arid areas, which are located at WSW direction, and 34.42 % of trajectories passed from the WNW direction. In August, at altitudes of 2500 m, in average arid WSW part in the trajectory ensemble was equal to 24.22 %, and the wet WNW part from the swamps of Western Siberia was equal to 41.05 %. It was also shown that the air masses that passed in July and August 2010–2019 over the polluted industrial agglomerations, that are allocated in the south of Beijing, did not passed over the area of the Lake Baikal, and therefore these air masses can not participate both in the ecological situation change in the area of the Lake Baikal, and in the processes of nuclearization at the cloud formation. For the period of 2010–2019 the average value of the SSE (South-South-East) part in the trajectory ensemble, which is corresponding to the transfer from the Central regions of China, was equal at an altitude of 250 m: in July to 1.00 %, in August to 0.49 %. At an altitude of 2500 m SSE sector in July was equal to 1.77 % and in August – 2.09 % of the total number of studied trajectories.

Understanding the relationship between urban fabrics and sustainability is critical for addressing contemporary urban challenges. Urban fabrics contain information that reveals the performance and spatial heterogeneity of urban systems. This study evaluates the variability and spatial patterns of sustainability indicators across different urban fabric typologies in Cuenca, Ecuador. Using a 150-radius hexagonal grid covering the study area, 30 sustainability indicators are assessed in this study, exploring their spatial patterns and relationships with urban fabrics trough spatial and statistical analyses and visualization techniques. Results reveal significant variability among urban fabrics, with built environment indicators playing a dominant role in overall sustainability. There is evidence of marked spatial heterogeneity, with inner-core areas showing higher sustainability, while fringe expansion areas lag, and transitional areas also identified. Spatial clusters of high or low sustainability fabrics tend to be homogeneous, whereas mid-range sustainability fabrics exhibit greater heterogeneity. This quantitative evidence highlights hidden patterns in urban morphology and sustainability, offering valuable insights to support and enhance evidence-based urban planning. The open-source data and tools reported are developed to allow for customisation as well as replication in other urban areas.

This study investigates the evolution of the vegetation and beach dune system at Enebrales Beach in Huelva, Spain, from 2003 to 2022. Utilizing orthophotographs and advanced GIS techniques, we analyzed changes in natural surface areas, vegetation cover, and dune stability. The results demonstrate significant ecological improvements: a recovery of 25,300 m² in dunes and 7,400 m² in beach areas, and a total vegetation cover increase of nearly 10,000 m². Interventions, including reforestation with native species and the stabilization of dune ridges, have been effective in enhancing the coastal ecosystem's resilience. Additionally, the beach's carrying capacity increased, allowing for approximately 2,000 more users without compromising ecological integrity. These findings underscore the importance of continued conservation efforts and adaptive management to maintain and further improve the ecological health and sustainability of Enebrales Beach Dune.

The Klaten Regency, Central Java Province, Indonesia is an leptospirosis endemic area. This study’s purpose is to spatially describe the abiotic and biotic environmental factors with the Leptospirosis incidence in the Klaten Regency in 2018. This was a descriptive observational with cross-sectional approach conducted in the Klaten Regency, Central Java, in 2019 with 59 respondents. The results revealed that the percentage of abiotic environment factors such as poor waste disposal facilities, poor gutter conditions, river <200 meters, and flooding history, namely 35.6%; 41.2%; 54.2%; and 6.8%, respectively. The highest leptospirosis cases occurred in April 2018 with 325 mm of rainfall, average temperature of 270C, average humidity of 82.3%, and altitude of 100-200 MASL (79.7%). Meanwhile, biotic factors included rat nest existence (100%), having pets at risk (32.2%), and ≥3 types of vegetation (79.7%). In conclusion, most respondents' dominant environmental factors were presence of rat nests, ≥3 vegetation types, and river availability <200 meters.

Introduction: Bovine tuberculosis is a zoonotic disease of considerable impact, especially in livestock countries, which can cause human disease. Despite its importance, Colombia is a country in which few studies analyse its behaviour, and none develop maps using geographic information systems to characterise it. Objective: To describe the temporal-spatial distribution of bovine tuberculosis in Colombia between 2001 and 2019. Methods: Retrospective cross-sectional descriptive study, based on reports by the Colombian Agricultural Institute, of tuberculosis surveillance in cattle on farms in Colombia from 2001 to 2019. The data were converted into databases in Microsoft Access 365®, and multiple epidemiological maps were generated with the Kosmo RC1®3.0 software coupled to shape files of all the country's departments. Results: During the study period, 5,273 tuberculosis cases in bovines were identified in the different departments of Colombia (mean 278 cases/year). Regarding its temporal distribution, the number of cases varied from a maximum of 903 cases (17.12% of the total) in 2015 to a minimum of 0 between 2001 and 2004 and between 2017 and 2019 (between 2005 and 2016, the minimum was 46 cases, 0.87%). Conclusions: GIS is an essential tool for understanding the temporospatial behaviour of zoonotic diseases in Colombia, as is the case of bovine tuberculosis, with its potential implications for Human and One Health.

Remote Sensing and Geographic Information System are recognized as crucial instruments for conducting geographical and geospatial research. RS and GIS technology was employed to assess the morphological Characteristics of the Kasura River Watershed situated within the Parbhani and Jalna Districts of Maharashtra, India. The entire drainage area of the Kasura River basin measures 490.13 square kilometers (km²). Various morphometric characteristic were calculated and examined, encompassing linear features such as stream number, stream order, mean stream length, stream length, and stream length ratio, areal attributes including elongation ratio, circularity ratio, form factor, constant of channel maintenance, drainage density and drainage texture and relief characteristics such as relief, relief ratio, maximum relief, ruggedness number, relative relief, and length of overland flow. These findings underscore the significance of employing morphometric analysis in watershed assessment, offering a quantitative method for characterizing the surface attributes of a drainage pattern and yielding valuable insights into the topography and runoff dynamics of the region.

(1) Background: Childhood immunization is vital for preventing morbidities and mortalities globally. However, rural Pakistan is facing persistent challenges, particularly after recent global health crises in gauging accurate vaccination coverage estimates. This study aimed to apply a novel Lot Quality Assurance Sampling (LQAS) methodology due to its rapid and reliable estimates of the routine immunization rates among children aged 12-23 months old in Shikarpur, Sindh, and to identify priority areas for future interventions. (2) Methods: A cross-sectional household survey design was adopted for an in-depth assessment of vaccination coverage in a previously under-studied rural context. (3) Results: Data were collected from 1,402 children aged 12-23 months across 47/49 Union Councils in Shikarpur within 141 randomly identified clusters. LQAS was innovatively employed along with GIS Mapping which provided a spatial analysis of the distribution of immunization coverage and the spot map of clusters. The weighted average for fully immunized children was 42.4% after applying Direct Adjustment Method. A steep decline in coverage for each successive vaccine dose was observed, and 39 key priority areas were identified on GIS-based plotting for intensive health interventions. Multivariate Logistic Regression Model further revealed informational gaps and fear of side effects as major barriers to achieving complete immunization. (4) Conclusions: The innovative application of LQAS and GIS Mapping in this study has provided a comprehensive glimpse of its utility in future follow-ups and similar assessments. The findings stress the critical need to tackle the foundational reasons behind the vaccination gaps, with a special focus on enhancing awareness and information dissemination in the key priority areas.

Recreational and leisure areas play a special role. These areas mitigate or eliminate the disadvantages of living in large cities, and they enhance the quality of life in small towns. The aim of this study was to analyze the share of recreational and leisure areas in Polish counties in 2022. In Poland, recreational and leisure areas constitute a separate land-use category in cadastral registers. Annual cadastral data from the Land and Building Register at county level (in tabular form) were the main source of data for the study. The analysis revealed that the share of recreational and leisure areas was higher in counties situated in western and south-western Poland, as well as in cities. The spatially varied influence of socioeconomic factors on the share of recreational and leisure areas in Polish counties in 2002 was determined with the use of local Moran's I statistic and geographically weighted regression (GWR). The study confirmed that population density was significantly related to the share of recreational and leisure areas in Polish counties. The impact of the remaining socioeconomic factors associated with spatial and economic development varied across regions. The study also revealed that in addition to the current socioeconomic determinants, the share of recreational and leisure areas in Polish counties was also influenced by historical factors and the counties' development since their establishment.

Leakages in the water distribution networks (WDNs) are real problems for utilities and other governmental agencies. Timely leak detection and location identification has been a challenge. In this paper, an integrated approach to geospatial and infrared image processing method was used for robust leak detection. The method combines drops in flow, pressure, and chlorine residuals to determine potential water leakage locations in the WDN using Geographic Information System (GIS) techniques. GIS layers were created from the hourly values of these three parameters for the city of Sharjah provided by Sharjah Electricity, Water and Gas Authority (SEWA). These layers are then analyzed for locations with dropped values of each of the parameters and are overlaid with each other. In the case where there were no overlaying locations between flow and pressure, further water quality analysis was avoided, assuming no potential leak. In the case where there are locations with drops in flow and pressure layers, these overlaying locations are then examined for drops in chlorine values. If overlaying locations are found, then these regions are considered potential leak locations. Once potential leak locations are identified, a specialized remote sensing technique can be used for precise leak location. This study also demonstrated the suitability of using an infrared camera for leak detection in a laboratory-based setup. This paper concludes that the following methodology can help water utility companies in the timely detection of leaks, saving money, time, and effort.

The province of Jaén (Andalusia, Spain), despite being declared the European territory with the largest number of defensive constructions (castles, fortifications, etc.), has few conservation plans, with many remains included on the Red List of Spanish Heritage ly-ing abandoned. This poses a challenge for landscape conservation and the optimal use of the province’s tourism potential. In this paper we present the early implementation of an ongoing methodological approach designed to alleviate this situation. It takes advantage of data from the Sentinel 2A and 2B satellites, HBIM, RPAS, GIS and fieldwork that will be applied across five archaeological case studies. It has been tested for the first time in the village of Magaña. Three main objectives and phases of this methodological approach have been designed: 1. Preparation of an Environmental Risk Chart with which to answer such ques-tions as “How have climate change, anthropic alterations and environmental characteris-tics affected the state of conservation of certain heritage sites?” 2. Preparation of HBIM models to foster monitorisation and conservation policies for the main archaeological remains and improving their protection based on digital and technological tools. 3. Building a Smart Tourism App for mobile devices aimed at promoting smart tourism by digitalising and virtualising tourist itineraries and archaeological remains. Finally, public administrations will be apprised of the need to implement a conservation policy for cultural assets and their surroundings in a simple, quick and cost-effective man-ner.

Shallow landslides pose serious threats to human existence and economic development, especially in the Himalayan areas. Landslide susceptibility mapping (LSM) is a proven way for minimizing the hazard and risk of landslides. Modeling as an essential step, various algorithms have been applied to LSM. In this study, information value (IV) and logistic regression (LR) were selected as representatives of the conventional algorithms, categorical boosting (CatBoost) and conventional neural networks (CNN) as the advanced algorithms, for LSM in Yadong county, and their performance was compared. To begin with, 496 historical landslide events were compiled into a landslide inventory map, followed by a list of 11 conditioning factors, forming a data set. Secondly, the data set was randomly divided into two parts, 80% of which was used for modeling and 20% for validation. Finally, the area under the curve (AUC) and statistical metrics were applied to validate and compare the performance of the models. The results showed that the CNN model performed the best (AUC 0.974 and accuracy=93.3%), while the LR model performed the worst (AUC 0.974 and accuracy=93.3%) and CatBoost model performed better (AUC 0.974 and accuracy=93.3%). Besides, the LSM constructed by the CNN model did a more reasonable prediction of the distribution of susceptible areas. As for feature selection, did a more detailed analysis of conditioning factors but the results were uncertain. The result analyzed by GI may be more reliable but fluctuates with the amount of data. The conclusion reveals that the accuracy of LSM can be further improved with the advancement of algorithms, by determining more representative features, which serve as a more effective guide for land use planning in the study area or other highlands where landslides are frequent.

Environmental degradation is one of the most visible problems in Hargeisa. Currently, solid waste is disposed of at two dumping sites within the city limits causing nuisance and unsanitary conditions. Moreover, the existing dumpsites are on the verge of closure, therefore necessitating a dire need to be addressed. This research paper is aimed to integrate multicriteria decision analysis and GIS for evaluating the site suitability for the landfill in Hargeisa, Somaliland. For this purpose, eleven significant parameters were selected: proximity from built-up areas, surface water, groundwater well points, sensitive sites (Airports), land use/land cover, geology, soil type, elevation, slopes, roads, and distance to existing dumpsites. These were then integrated using an analytical hierarchy process (AHP). Subsequently, restriction buffer analysis was performed on the seven parameters to obtain better and more accurate results, and restricted zones were omitted. Furthermore, the pair-wise comparison used to obtain priorities between the selected criteria showed that the land uses land cover is the most significant criterion in the model with a relative weight of 0.1829, followed by the habitations, 0.1506. The overall result reveals that about 68.96% (21060.9 ha) of the study area is unsuitable, while 24.36% (7441.53 ha) and 6.68% accounted for the less and highly appropriate zone, respectively. Following the results of this study, Hargeisa City's municipal council must reconsider the waste management and landfill sites to solve the problems observed in the area before it is overdue. Furthermore, this systematic research approach will assist regional and global researchers, policymakers, and municipal governments.

Many spatial decision support systems suffer from user adoption issues in practice due to lack of trust, technical expertise, and resources. Automated machine learning has recently allowed non-experts to explore and apply machine learning models in the industry without requiring abundant expert knowledge and resources. This paper reviews recent literature from 136 papers, and proposes a general framework for integrating spatial decision support systems with automated machine learning to lower major user adoption barriers. Challenges of data quality, model interpretability, and practical usefulness were discussed as general considerations for system implementation. Research opportunities related to spatially explicit models in AutoML, and resource-aware, collaborative/connected, and human-centered systems were also discussed to address these challenges. This paper argues that integrating spatial decision support systems with automated machine learning can not only encourage user adoption, but also mutually benefit research in both fields — bridging human-related and technical advancements for fostering future developments in spatial decision support systems and automated machine learning.