Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Smart Cities Planning and Sustainable Urban Development through Development and Analyzing Mental Maps of Citizen Perceptions

Farshid Aram
ORCID logo ,
Ebrahim Solgi
,
Ester Higueras Garcia
,
Danial Mohammadzadeh S.
ORCID logo ,
Amir Mosavi
ORCID logo ,
Shahaboddin Shamshirband
* ORCID logo
Version 1 : Received: 12 June 2019 / Approved: 17 June 2019 / Online: 17 June 2019 (10:12:15 CEST)

A peer-reviewed article of this Preprint also exists.

Aram, F.; Solgi, E.; Higueras García, E.; Mohammadzadeh S., D.; Mosavi, A.; Shamshirband, S. Design and Validation of a Computational Program for Analysing Mental Maps: Aram Mental Map Analyzer. Sustainability 2019, 11, 3790. Aram, F.; Solgi, E.; Higueras García, E.; Mohammadzadeh S., D.; Mosavi, A.; Shamshirband, S. Design and Validation of a Computational Program for Analysing Mental Maps: Aram Mental Map Analyzer. Sustainability 2019, 11, 3790.

Abstract

Considering citizens' perceptions of their living environment is very helpful in making the right decisions for city planners who intend to build a sustainable society. Mental map analyses are widely used in understanding the level of perception of individuals regarding the surrounding environment. The present study introduces Aram Mental Map Analyzer (AMMA), an open-source program, which allows researchers to use special features and new analytical methods to receive outputs in numerical data and analytical maps with greater accuracy and speed. AMMA performance is contingent upon two principles of accuracy and complexity, the accuracy of the program is measured by Accuracy Placed Landmarks (APL) and General Orientation (GO), which respectively analyses the landmark placement accuracy and the main route mapping accuracy. Also, the complexity section is examined through two analyses Cell Percentage (CP) and General Structure (GS), which calculates the complexity of citizens’ perception of space based on the criteria derived from previous studies. AMMA examines all the dimensions and features of the graphic maps, and its outputs have a wide range of valid and differentiated information, which is tailored to the research and information subject matter that is required.

Keywords

Smart urban planning; sustainable urban development; mental maps; smart cities; quantitative analysis; environmental psychology; landmark; sustainable society

Subject

Computer Science and Mathematics, Information Systems

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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