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

Author

Listed:
  • Farshid Aram

    (Escuela Técnica Superior de Arquitectura, Universidad Politécnica de Madrid-UPM, 28040 Madrid, Spain)

  • Ebrahim Solgi

    (School of Engineering and Built Environment, Griffith University, Gold Coast 4215, Australia)

  • Ester Higueras García

    (Escuela Técnica Superior de Arquitectura, Universidad Politécnica de Madrid-UPM, 28040 Madrid, Spain)

  • Danial Mohammadzadeh S.

    (Department of Civil Engineering, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran
    Department of Elite Relations with Industries, Khorasan Construction Engineering organization, Mashhad 9185816744, Iran)

  • Amir Mosavi

    (Faculty of Health, Queensland University of Technology, Brisbane QLD 4059, Australia
    Kando Kalman Faculty of Electrical Engineering, Obuda University, 1034 Budapest, Hungary
    School of the Built Environment, Oxford Brookes University, Oxford OX3 0BP, UK)

  • Shahaboddin Shamshirband

    (Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh, Vietnam
    Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam)

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.

Suggested Citation

  • Farshid Aram & Ebrahim Solgi & Ester Higueras García & Danial Mohammadzadeh S. & Amir Mosavi & Shahaboddin Shamshirband, 2019. "Design and Validation of a Computational Program for Analysing Mental Maps: Aram Mental Map Analyzer," Sustainability, MDPI, vol. 11(14), pages 1-20, July.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:14:p:3790-:d:247374
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    References listed on IDEAS

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    Cited by:

    1. Sina Ardabili & Amir Mosavi & Asghar Mahmoudi & Tarahom Mesri Gundoshmian & Saeed Nosratabadi & Annamaria R. Varkonyi-Koczy, 2020. "Modelling Temperature Variation of Mushroom Growing Hall Using Artificial Neural Networks," Papers 2010.02673, arXiv.org.
    2. Reza Askarizad & Jinliao He, 2022. "Perception of Spatial Legibility and Its Association with Human Mobility Patterns: An Empirical Assessment of the Historical Districts in Rasht, Iran," IJERPH, MDPI, vol. 19(22), pages 1-17, November.
    3. Mehrdad Chahardowli & Hassan Sajadzadeh & Farshid Aram & Amir Mosavi, 2020. "Survey of Sustainable Regeneration of Historic and Cultural Cores of Cities," Energies, MDPI, vol. 13(11), pages 1-21, May.
    4. Sadegh Fathi & Hassan Sajadzadeh & Faezeh Mohammadi Sheshkal & Farshid Aram & Gergo Pinter & Imre Felde & Amir Mosavi, 2020. "The Role of Urban Morphology Design on Enhancing Physical Activity and Public Health," IJERPH, MDPI, vol. 17(7), pages 1-29, March.
    5. Saeed Nosratabadi & Amir Mosavi & Ramin Keivani & Sina Ardabili & Farshid Aram, 2020. "State of the Art Survey of Deep Learning and Machine Learning Models for Smart Cities and Urban Sustainability," Papers 2010.02670, arXiv.org.
    6. Farshid Aram & Ebrahim Solgi & Ester Higueras García & Amir Mosavi & Annamária R. Várkonyi-Kóczy, 2019. "The Cooling Effect of Large-Scale Urban Parks on Surrounding Area Thermal Comfort," Energies, MDPI, vol. 12(20), pages 1-21, October.

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