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Design and Validation of a Computational Program for Analysing Mental Maps: Aram Mental Map Analyzer

1
Escuela Técnica Superior de Arquitectura, Universidad Politécnica de Madrid-UPM, 28040 Madrid, Spain
2
School of Engineering and Built Environment, Griffith University, Gold Coast 4215, Australia
3
Department of Civil Engineering, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran
4
Department of Elite Relations with Industries, Khorasan Construction Engineering organization, Mashhad 9185816744, Iran
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Faculty of Health, Queensland University of Technology, Brisbane QLD 4059, Australia
6
Kando Kalman Faculty of Electrical Engineering, Obuda University, 1034 Budapest, Hungary
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School of the Built Environment, Oxford Brookes University, Oxford OX3 0BP, UK
8
Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh, Vietnam
9
Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam
*
Author to whom correspondence should be addressed.
Sustainability 2019, 11(14), 3790; https://doi.org/10.3390/su11143790
Received: 12 June 2019 / Revised: 27 June 2019 / Accepted: 1 July 2019 / Published: 11 July 2019
(This article belongs to the Special Issue Smart Urban Planning and Land Management)
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PDF [8051 KB, uploaded 12 July 2019]
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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. View Full-Text
Keywords: Smart urban planning; sustainable urban development; mental maps; smart cities; quantitative analysis; environmental psychology; landmark; sustainable society Smart urban planning; sustainable urban development; mental maps; smart cities; quantitative analysis; environmental psychology; landmark; sustainable society
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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 (CC BY 4.0).
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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.

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