Applying Area Cartograms to Visualize Sustainable Development Goals Indicators Based on Earth Observation Data

1. Introduction

Monitoring the achievement of the Sustainable Development Goals (SDGs) is an important topic that can be assisted by using indicators elaborated based on Earth Observation (EO) data. The United Nations (UN) Sustainable Development Goals are global development targets adopted in 2015. All countries have agreed to work towards achieving them by 2030 [1]. The Sustainable Development Goals (SDGs) form a closely linked set of priorities, where progress in one area often affects outcomes in others. Reaching these goals requires finding a practical balance between environmental protection, economic growth, and social well-being. Their implementation depends not only on political will but also on the involvement of institutions, local governments, and civil society. To monitor progress, the UN Secretary-General releases an annual report prepared in collaboration with the UN system, using data collected by national statistical offices and regional partners [2]. These reports contain information on the realization of particular SDG goals, targets, and indicators, part of which is presented on maps. Statistics Poland (SP) is responsible for presenting the implementation of the SDGs in Poland. On the Institution's website, it is possible to find information about 250 global indicators with data for Poland for monitoring the Sustainable Development Goals (SDGs).

The study presents indicators related to SDG11 (Goal 11 – Sustainable cities & communities), and SDG13 (Goal 13 – Climate action). As a preliminary step, the following targets and indicators were selected for analysis:

• Target 11.6: Reduce the environmental impacts of cities (Indicator 11.6.2 – Annual mean levels of fine particulate matter);
• Target 11.7: Provide access to safe and inclusive green and public spaces (Indicator 11.7.1 – Average share of the built-up area of cities that is open space for public use for all);
• Target 13.2: Integrate climate change measures into national policies, strategies, and planning (Indicator 13.2.2 – Total greenhouse gas emissions per year).

Air pollution remains a major challenge in urban areas. Elevated concentrations of particulate matter (PM), nitrogen oxides (NO₂), tropospheric ozone (O₃), sulfur dioxide (SO₂) are known to negatively impact human health and quality of life in affected regions. These pollutants also contribute to the degradation of biodiversity and the disruption of ecosystem functions. According to the World Health Organization (WHO) and the European Environment Agency (EEA), long-term exposure to air pollution is associated with reduced life expectancy and increased rates of premature mortality. These concerns are reflected in the Sustainable Development Goals, particularly Target 11.6, which aims to reduce the per capita environmental impact of cities, with a focus on air quality and waste management. The related Indicator 11.6.2 measures the annual mean levels of fine particulate matter (PM2.5 and PM10) in urban areas, weighted by population. In the context of air quality monitoring (SDG 11.6.2 and 13.2.2, Figure 1.), advanced earth observation (EO) techniques are employed, integrating multi-temporal Copernicus Sentinel-5P satellite data with deep learning models. This approach monitors pollutants such as NO₂, SO₂, tropospheric ozone, CO, CH₄, CH₂O, and particulate matter (PM2.5, PM10) [3]. These products are supplemented with meteorological data and ground-based measurements. Deep learning models enable precise classification of pollution levels, combining satellite and in situ data for improved accuracy [4]. For studies conducted in Poland, satellite-derived products are validated using national monitoring networks (e.g., the Chief Inspectorate of Environmental Protection), ensuring their reliability [5]. Within this framework, detailed spatial and temporal pollution maps can be developed using GIS and geoinformation platforms. Such maps support urban management, public health planning, and policy-making, particularly in areas with limited ground-based monitoring, contributing to effective and scalable air quality assessment aligned with SDGs targets.

Green areas, addressed in SDG Indicator 11.7.1, are particularly important in densely populated cities, where they contribute to local climate regulation and provide accessible spaces for recreation. In addition to their environmental functions, green spaces support public health and social interaction, which can strengthen neighborhood ties. As a result, they play a measurable role in enhancing the quality of life in urban environments [7,8,9]. Recognizing their significance, there is a growing need to develop comprehensive and up-to-date systems to monitor and assess green areas, enabling informed decision-making for sustainable urban development [10,11,12]. The Sustainable Development Goals (SDGs) focus on creating sustainable cities and communities, emphasising the importance of green areas, e.g., SDG Target 11.7: “By 2030, provide universal access to safe, inclusive and accessible, green and public spaces in particular for women and children, older persons and persons with disabilities” as an integral component of urban planning and development. Urban green space, such as parks and gardens, play a crucial role in enhancing the well-being of urban residents, promoting environmental health, and contributing to overall urban resilience [13,14,15,16]. In the literature, numerous studies are available that utilize remote sensing to assess the availability of green areas within the framework of the Sustainable Development Goals in Poland [17,18]. Within the GAUSS project [6], (Figure 2), a green space assessment system was developed by integrating Sentinel-2 satellite data, vegetation products from the Copernicus Land Monitoring Service, and in situ information. To ensure accurate and reliable validation, the database of topographic objects (BDOT10k) was utilized, enabling detailed characterization of green spaces across Poland. By combining these diverse data sources, the developed service allows end-users to effectively monitor green spaces through the generation of annual statistics accompanied by statistical maps of Poland at the municipal level.

2. Related Studies

An analysis of 120 selected publications showed that 19 different cartographic methods were applied to visualize the SDG indicators 11.6.2, 11.7.1, and 13.2.2. (Figure 3). Only in five cases dasymetric solutions [19,20,21,22,23] and in two cases – a cartogram [24,25] were employed. Almost all cases were static solutions. Only in one case a comparison of multiple time states [26] was employed to visualize the greenhouse gas emissions in China in 2000-2011 period. Only one interactive solution was used [27] to present the average air quality map of the greater Cleveland area.

It should be emphasized that the use of cartographic methods differs significantly in the case of visualization of the three analyzed indices. For the 11.6.2 indicator visualization, the shaded isolines method is used in more than 43% of cases e.g.: [28,29,30,31,32,33,34,35,36,37,38]. Two cases of its combined use with bars have been identified [39,40] and only one case of combination with choropleth map [41]. Relatively often (28%), a grid with a different size of the basic field was used as a cartographic presentation method for the visualization of the 11.6.2 indicator [42,43,44,45,46,47,48,49]. Sometimes it was limited to the area of communication routes (e.g.: [49]). There have been cases of it being combined with choro-pleth maps (4%, e.g.: [48]), as well as with shaded isolines (2%, e.g.: [44]). Much less frequent (4%) were the cases of using quantitative signatures (e.g.: [51,52]). One case of combined use of quantitative signatures with grid and choropleth method was identified [53]. Relatively rare were the cases of using the choropleth maps (e.g.: [54]), line choropleth maps (e.g.: [55]), cross choropleth maps (e.g.: [56]), as well as the combined use of choropleth maps with ordinary signatures (e.g.: [57]). Only 2 % of the solutions involved the use of isolines (e.g.: [58]) and vector maps (e.g.: [59]). In most cases, it is used to present the location of green areas in cities.

In the case of the 11.7.1 indicator, the most frequently used solution is the choro-chromatic method, used in 38% of publications (e.g.: [60,61,62,63,64,65,66] ). 16% are applications of the choropleth method, with units related to city districts (e.g.: [67,68,69,70,71,72]. Isochrone (e.g.: [73,74,75,76]) and grid (e.g.: [77,78,79,80,81]) applications constitute 8% each. Quantitative point signatures are used in 6% (e.g.: [82,83])., while buffer (e.g.: [84,85]), qualitative point signatures (e.g.: [86,87]) and dasymetric choropleth maps (e.g.: [19]) – only 4% each. The least frequently used methods for 11.7.1 indicators are: linear choropleth maps ([88]) proportional symbols (e.g.: [89]), shaded isolines (e.g.: [90]) and radius maps (e.g.: [91]).

The relatively smallest diversity of methods used for visualization occurs in the case of indicator 13.2.2. The most commonly used are choropleth map (e.g.: [92,93,94,95]) and grid (e.g.: [96,97]) − 35% each. In addition, the following methods are used: shaded isolines [98], structural diagrams [99], dasymetric method [100] and cartogram [101]. Depending on the aggregation of available data, indicators are visualized in relation to different spatial units.

In the case of 11.6.2 SDG indicator 50% of maps were elaborated using the point data, which were mostly employed to build the isoline maps (43 %). The other 7 % point data (7 %) allowed to use quantitative point signatures. 28 % of maps employed different grid cells like a spatial units. In 2% of cases the data was related to the NUTS 3. The same percentage was found in the case of city districts and functional areas of cities. In the 4,35 % of cases the spatial units were dense grid of streets. The same percentage was used in attempts to link the index value with land cover elements in cities.

In the case of 11.7.1 SDG indicator 38% of maps were elaborated using the land use data concerning the cities. In 24% of cases, the spatial reference for this indicator was the division units. 10% of them were city districts and 4% − city functional areas. In 4% of cases, the analysis covered areas within functional areas, while in 6% of cases, the analysis covered areas bounded by streets. Grid with different basic fields was used in 10% of the maps presenting the 11.7.1 indicator.

In the case of 13.2.2 SDG indicator 45% maps were elaborated using remote sensing data, concerning the entire Earth. In 25% of cases the data was related to countries (NUTS 0), in 20% to NUTS 2 and only in 10% to NUTS 3. In 5% the 13.2.2 SDGs indicators were related to the global land use data.

It should be emphasized that in the case of analyzed maps of 11.6.2, 11.7.1 13.2.2 SDGs indicators there was no case of referring data from the EO registration to the smallest units of administrative division at the area of whole country. This solution was used in the case of the article [6].

A critical examination of the relevant literature indicates that cartograms have been employed only infrequently in the representation of sustainable development indicators at the municipal and county levels, highlighting a notable gap in spatial visualization practices at finer administrative scales.

3. Materials and Methods

This study examines area cartograms employed for monitoring changes in Sustainable Development Goal (SDG) indicators using earth observation (EO) data. The objective is to assess the respective advantages and limitations of this approach in the context of tracking progress toward the SDGs. The prepared maps focus on two key themes aligned with SDG 11 and SDG 13: urban green spaces and air pollution (PM2.5, PM10, NO_x). Both the spatial extent and condition of green areas, as well as the concentrations of selected air pollutants, were derived from Earth observation data. The applied methodology have been described in Introduction.

3.1. Study Area

As part of the study, statistical maps were generated to illustrate data for Poland at various administrative levels: at the commune (gmina) level (Figure 4 left) and the county (powiat) level (Figure 4 right). Poland is a country in Central Europe, covering an area of approximately 312,696 square kilometers. Its administrative structure is organized into three main tiers: 16 voivodeships (provinces), 380 counties (poviats), and 2,477 communes (gminas). This territorial division reflects a decentralized governance model aimed at improving administrative efficiency at regional and local levels.

3.2. Topic

3.2.1. Air Polution

Particulate Matter with a diameter ≤ 2.5 µm (PM2.5) refers to fine particulate matter with an aerodynamic diameter of 2.5 micrometers or smaller [102]. These particles are small enough to penetrate deep into the respiratory tract and reach the alveoli, posing significant health risks. PM2.5 originates from both natural sources, such as wildfires and dust storms, and anthropogenic (human-made) activities, including vehicle emissions, industrial processes, and residential combustion. Secondary PM2.5 can also form in the atmosphere through chemical reactions involving precursors such as sulfur dioxide (SO₂), nitrogen oxides (NOₓ), and volatile organic compounds (VOCs). Due to their small size and chemical composition, PM2.5 particles are a major concern for air quality and public health monitoring.

Particulate Matter with a diameter ≤ 10 µm (PM10) refers to a mixture of solid particles and liquid droplets suspended in the air. These particles are small enough to be inhaled and can penetrate the respiratory system, potentially causing adverse health effects. PM10 originates from various sources, including road traffic, industrial processes, and natural sources such as dust or pollen. It is commonly used as an indicator of general air quality, especially in urban environments.

Nitrogen oxides (NO_x) is a collective term for nitrogen monoxide (NO) and nitrogen dioxide (NO₂), two major air pollutants produced primarily during high-temperature combustion processes, such as those in vehicle engines and power plants. NO_x plays a significant role in atmospheric chemical reactions, contributing to the formation of ground-level ozone, smog, and secondary particulate matter. These compounds are harmful to human health, particularly affecting the respiratory system, and also contribute to environmental problems such as acid rain and eutrophication. In air quality assessments, NO_x is a key indicator of combustion-related pollution, especially in urban and industrial areas.

3.2.2. Green Areas

The maps presented in this publication were prepared using definitions of green areas derived from Nature Protection criteria. According to the Polish Act [103] green areas including technical infrastructure and buildings functionally associated with them, covered with vegetation, located in the village of dense buildings or cities, used for the aesthetic, recreational, therapeutic or shielding purposes, in particular parks, lawns, promenades, boulevards, botanical and zoological gardens, children's playgrounds, historic garden, cemeteries, green areas located near roads in the build-up areas, squares, historic fortifications, buildings, landfill sites, airports, railway stations and industrial buildings. The application of this definition enables the classification of green areas in relation to SDG 11.

3.3. Data Sources

3.3.1. Air Polution

The Copernicus Atmosphere Monitoring Service (CAMS) reanalysis is the most recent global reanalysis dataset of atmospheric composition (AC) developed under the European Union’s Copernicus Earth observation programme. It provides three-dimensional, time-consistent fields of key atmospheric constituents, including aerosols, reactive gases, and greenhouse gases. The dataset benefits from methodological advancements and operational experience gained through the preceding Monitoring Atmospheric Composition and Climate (MACC) reanalysis and the CAMS interim reanalysis. The current dataset spans the period from 2003 to December 2023. CAMS reanalysis data are provided at a horizontal resolution of approximately 80 km, with both sub-daily and monthly temporal resolution, and cover a wide range of atmospheric variables [104].

To obtain yearly averages of air pollutants (PM2.5, PM10, NO_x), datasets from CAMS and GUGiK were utilized. The process involved converting hourly pollution data from NetCDF to TIFF format, cropping it to Poland’s borders, and calculating monthly averages for each pixel. These monthly images were then resampled, stacked, and used to compute average pollutant concentrations for local administrative units (LAUs). Finally, yearly averages were calculated from monthly data for each pollutant, combined into a single dataset, and converted into a vector file for analysis and mapping.

3.3.2. Green Areas

The study produced maps focusing on two fundamental attributes of urban green spaces: their distribution and health status. The extent of green areas was delineated using Sentinel-2 data and products from the GAUSS project [6], with a spatial resolution of 10 meters. To achieve a more precise delineation of green areas at the commune level, in addition to satellite data, detailed vector boundaries from BDOT10k (Topographic Objects Database at a 1:10,000 scale) were used. Sentinel-2 products were also utilized to assess the condition of urban green spaces.

Values of the widely used Normalized Difference Vegetation Index (NDVI) were derived from Sentinel-2 data to enable comparison with a novel vegetation index based on High-Resolution Vegetation Phenology and Productivity (HR-VPP) data. Subsequently, the data on the extent and condition of green areas were aggregated to the commune level (LAUs). Depending on the chosen cartographic presentation method (choropleth maps, or area cartograms) additional statistical data from the Local Data Bank (Central Statistical Office) were incorporated. These supplementary datasets included demographic information, such as population by age groups at the commune level, and vehicle counts at the county level.

3.4. Map Production

3.4.1. Colour Legend

An integral component of the map development process was the creation of legends for the choropleth maps illustrating air pollution. Reference ranges for pollutant particle concentrations and their corresponding map colors were adopted from the literature (Table 1.). These color scales were subsequently adjusted to better reflect the dataset. In the later stages of map preparation, additional subdivisions were introduced within the scales while maintaining the original color scheme—for example, the 'good air quality' category was refined by incorporating lighter and darker shades of green.

3.4.2. Choropleth Maps

All choropleth maps were created using QGIS Desktop, version 3.38.0. The underlying base map was the 2020 administrative division of Poland, delineated at the county (powiat) or municipality (gmina) level. For all choropleth maps, the 'natural breaks' (Jenks) classification method was applied, meaning that class intervals were optimized based on the frequency distribution of the data in order to minimize within-class variance and maximize between-class variance.

3.4.3. Area Cartograms

The cartogram is a map on which one feature – distance (distance cartograms) or area (area cartograms) is distorted proportionally to the value of a given phenomenon [105]. This presentation addresses selected area cartograms, placing special emphasis on automatically generated rectangular cartograms. Various software tools can be used to generate contiguous area cartograms [106]. These may include dedicated software for designing cartograms, plugins or toolboxes for GIS applications, or scripts written in programming languages—particularly in R. A review of selected software tools that support the automated generation of contiguous area cartograms is presented in Table 2.

The irregular Gastner-Newman cartograms in this publication were produced using the QGIS Desktop plugin cartogram3 version 3.1.5. The resulting maps were generated over two iterations for municipalities (gminy) and five iterations for counties (poviaty), with a maximum mean error in the anamorphosis process not exceeding 15%.

4. Results

4.1. Air Polution

The initial set of maps focuses on air pollution issues related to Sustainable Development Goals (SDGs) 11.6 and 13.2. In the first phase, three choropleth maps were produced (Figure 5), illustrating the spatial distribution of PM2.5, PM10, and NO_x concentrations, respectively in LAUs in 2020. A uniform color scale was applied throughout, with green hues indicating the lowest levels of air pollution. The analysis of these maps indicates that, in 2020, the overall air quality in Poland was relatively good. However, elevated pollutant concentrations were recorded in major urban areas (e.g., Warsaw, Łódź), within the Upper Silesian Industrial Region (particularly in the case of PM2.5), and along key transportation corridors (especially NO_x, notably the belt extending from Warsaw and Łódź toward the Upper Silesian region). According to the spatial patterns presented on the maps, the areas with the lowest air pollution concentrations are located in northern Poland—especially along the Baltic Sea and in proximity to the Kaliningrad and Lithuanian borders—and in the mountainous regions of the south, such as the Bieszczady and Tatras.

The second step involved producing area cartograms to depict PM2.5 and PM10 concentrations within Polish municipalities in 2020 (Figure 6). The area of each municipality on the cartograms is scaled proportionally to its population. This approach was chosen because population size directly relates to emissions, as residential sources constitute the main contributors to both PM2.5 and PM10 pollution.

It can be observed that on the cartograms, larger municipal areas generally correspond to lighter shades of green or yellow. The use of cartograms distinctly highlights municipalities with large populations but small geographic areas and their associated pollution levels. On cartograms, major cities become distinctly visible, including Szczecin, the Tricity metropolitan area (Gdańsk), Poznań, Łódź, Warsaw, Wrocław, the Upper Silesian Industrial Region, and Kraków, roughly arranged from northwest to southeast. In contrast, these areas were less clearly represented in the previous standard choropleth maps (Figure 6), where cities like Szczecin, the Tricity, and parts of the Upper Silesian conurbation appeared less prominent.

In the NOx concentration cartogram, the territorial division of Poland into counties (poviaty) was used, with the area of each unit scaled according to the number of vehicles registered in that county in 2020 (Figure 7). Applying an area cartogram in this context produced a notably informative outcome: counties with the largest visual representation—reflecting the highest number of registered vehicles—are consistently shaded in the lightest green, corresponding to the highest NO_x concentrations. This spatial pattern is not as easily discernible in the standard choropleth map presented on the left side of Figure 7. By using the area cartogram, NO_x concentrations in the Tricity area became clearly discernible − something that was challenging on the choropleth map owing to the small size of the constituent urban counties.

Nevertheless, it is important to acknowledge that the application of area cartograms can result in considerable spatial distortions. This is especially evident in counties with large territorial extent but relatively few registered vehicles, which can complicate the interpretation of data in regions such as Eastern Poland.

4.2. Green Areas

The choropleth map (Figure 8) and area cartograms (Figure 8 and Figure 9) were generated for green areas to illustrate both their coverage and overall condition. Figure 8 presents the extent of green areas using a choropleth map, as well as both the extent and condition of green areas using a cartogram. In the choropleth map, increasingly intense shades of green indicate a higher proportion of urban green space relative to the total municipal area. In the cartogram, the intensity of the green shade corresponds to the condition of green spaces − the stronger the hue, the better the ecological state.

It is evident that in certain municipalities, including Warsaw and the Tricity, green areas represent a substantial proportion of the total municipal territory; however, their ecological condition ranges from moderate to poor. Such nuanced information cannot be as effectively discerned from the choropleth map alone.

In relation to Target 11.7 — 'Provide access to safe and inclusive green and public spaces' — a critical consideration is the assessment of accessibility for specific demographic groups, particularly children and the elderly. Figure 9 presents a set of cartograms illustrating both the excent and ecological condition of green areas in correlation with the number of children (aged 0–14) residing in each municipality. Findings indicate that in municipalities with relatively large child populations, the ecological condition of green spaces is not necessarily high. The use of area cartograms enhances the interpretive capacity of spatial analyses by simultaneously representing accessibility and environmental quality. This approach underscores the critical role of green space quality in supporting the health, development, and well-being of younger demographic groups.