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Identifying urban neighborhoods with higher potential for social investment using GIS-FIS approach

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Abstract

Today, participation in social investment is a critical concern of urban management in metropolitan areas. To be effective in both public and private sectors, such participation requires an accurate knowledge of geographical distribution of resources and social needs in different parts of the city in order to better manage these investments. To this end, the present research aimed at providing an objective and tangible integrated approach to identify urban neighborhoods with high potential for social investment. The research area was neighborhoods located in Mashhad, Iran. Thirty-one criteria in three dimensions of empowerment, education, and culture were identified. Having determined, normalized, and weighted the values of each criteria in different neighborhoods using the analytic hierarchy process (AHP) method, the weight scores of each neighborhood in each dimension were added up by GIS approach and Arc GIS 10.1 software. Then, in order to conclude the overall status of social investment in different neighborhoods, a fuzzy inference system (FIS) was designed in the MATLAB 2014a environment according to experts’ opinions. Finally, based on the output of the designed system, the overall status of each neighborhood studied was determined in terms of social investment and neighborhoods with high potential for social investment were identified. The approach presented provides an insight for urban mangers, companies, and philanthropic investors to invest more effectively given the appropriate social investment opportunities.

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References

  • Attanasi G, Casoria F, Centorrino S, Urso G (2013) Cultural investment, local development and instantaneous social capital: a case study of a gathering festival in the South of Italy. J Socio-Econ 47:228–247

    Article  Google Scholar 

  • Berry BJL, Marble DF (1968) Spatial analysis: a reader in geography. Prentice- Hall Incorporations, Englewood Cliffs

    Google Scholar 

  • Bonoli G (2013) The origins of active social policy: labor market and childcare policies in comparative perspective. Oxford University Press, New York

    Book  Google Scholar 

  • Bouget D, Frazer H, Marlier E, Sabato S, Vanhercke B (2015) Social investment in Europe. A study of national policies. European Commission, Brussels Google Scholar

    Google Scholar 

  • Cantillon B (2011) The paradox of the social investment state. Growth, employment and poverty in the Lisbon Era’. J Eur Social Policy (forthcoming).

  • Carley MJ (1981) Social measurement and social indicators: issues of policy and theory. Boston, Mass., George Allen & Unwin, 1981. x, 195 p. (Contemporary Social Research Series, No. 1)

  • Cutter SL, Finch C (2008) Temporal and spatial changes in social vulnerability to natural hazards. Proc Natl Acad Sci 105(7):2301–2306

    Article  Google Scholar 

  • Czogala E, Leski J (2000) Fuzzy and neuro-fuzzy intelligent systems, ser. Studies in Fuzziness and Soft Computing. Springer, 47

  • Esping-Andersen G (2002) A child-centered social investment strategy. In: Esping-Andersen G, Gallie D, Hemerijck A, Meyers J (eds) Why we need a new welfare state. Oxford University Press, New York, pp 26–67

    Chapter  Google Scholar 

  • Flanagan BE, Gregory EW, Hallisey EJ, Heitgerd JL, Lewis B (2011) A social vulnerability index for disaster management. J Homeland Security Emergency Manag 8(1):1–22

    Article  Google Scholar 

  • Foong KC, Chee CT, Wei LS (2009) Adaptive network fuzzy inference system (ANFIS) hand off algorithm. Int J Netw Mobile Technol 1(2):54–59

    Google Scholar 

  • Goodchild MF (1988) A spatial geographical perspective on GIS. Int J Geogr Inf Syst 1:327–334

    Article  Google Scholar 

  • Hagerstrand T (1985). Time-geography: focus on the corporeality of man, society, and environment. New York:United Nations Univ

  • Johnston RJ (1994) Spatial analysis. In: Johnston RJ, Gregory D, Smith DM (eds) The Dictionary of Human Geography. Blackwell, Oxford

    Google Scholar 

  • Kanbur R, Venables AJ (2005) Spatial inequality and developement overview of UNU-WIDER project. Working Papers 127127, Cornell University, Department of Applied Economics and Management

  • Kia SM (2011) Fuzzy inference in MATLAB. Kianpub, Tehran

    Google Scholar 

  • Mamdani EH, Assilian S (1975) An experimental in linguistic synthesis with a fuzzy logic controller. Int J Man-Machine Stud 7(1):1–13

    Article  Google Scholar 

  • Martines MR, Toppa RH, Ferreira RV, Cavagis A, Kawakubo FS, Morato RG (2017) Spatial analysis to identify urban areas with higher potential for social investment. J Geogr Inf Syst 9(5):591–603

    Google Scholar 

  • Mashhad city statistical yearbook (2016-2017). Bureau of statistics, research and strategic studies

  • McEwin M (1995) Social indicators and social statistics in Australia. Stat J U N Econ Comm Eur 12(3-4):309–318

    Google Scholar 

  • Midgley J (1999) Growth, redistribution and welfare: towards social investment. Soc Serv Rev 77(1):3–21

    Article  Google Scholar 

  • Midgley J (2017) Social welfare for a global era: international perspectives on policy and practice. Sage Publications, Los Angeles

    Book  Google Scholar 

  • Miles I (1985) Social indicators for human development. United Nations University

  • Morel N, Palier B, Palme J (2012) Towards a Social Investment Welfare State? Ideas, Policies and Challenges. Policy Press

  • Otieno OD (2016) Role of educational investment on economic growth and development in Kenya. J Educ Pract 7(22):68–81

    Google Scholar 

  • Pattison WD (1963) The four traditions of geography. J Geogr 36:166–161

    Google Scholar 

  • Rufat SE, Burton CG, Maroof AS (2015) Social vulnerability to floods: review of case studies and implications for measurement. Int J Disaster Risk Reduct 14(Part 4):470–486

    Article  Google Scholar 

  • Saaty TL (1980) The analytic hierarchy process. McGraw-Hill, New York

    Google Scholar 

  • Statistics of Mashhad City (2016). Mashhad: Mashhad municipality

  • Thapa RB, Murayama Y (2010) Drivers of urban growth in the Kathmandu valley, Nepal: examining the efficacy of the analytic hierarchy process. Appl Geogr 30:70–83

    Article  Google Scholar 

  • Titz A, Cannon T, Krüger F (2018) Uncovering ‘community’: challenging an elusive concept in development and disaster related work. Societies 8(3):1–28

    Article  Google Scholar 

  • Trondheim RJ (2002) Reducing disaster vulnerability through local knowledge and capacity. Thesis, Norwegian University, Department of Town and Regional Planning

  • Twigg J (2001) Sustainable livelihoods and vulnerability to disasters, Benfield Greig hazard Center, for the Disaster Mitigation Institute (DMI)

  • Uricchio VF, Giordano R, Lopez N (2004) A fuzzy knowledge-based decision support system for groundwater pollution risk evaluation. J Environ Manag 73(3):189–197

    Article  Google Scholar 

  • Wisner B (1998) Marginality and vulnerability; why the homeless of Tokyo don’t ‘count’ in disaster preparations. Appl Geogr 18(I):2.5–33

    Article  Google Scholar 

  • Wisner B, Blaikie P, Cannon T, Davis I (2004) At risk: natural hazards, people’s vulnerability and disasters. Routledge, London

    Google Scholar 

  • Yoon DK (2012) Assessment of social vulnerability to natural disasters: a comparative study. Nat Hazards 63(2):823–843

    Article  Google Scholar 

  • Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Department of Sustainable Development Urban and Regional Academic Center for Education, Culture and Research, Khorasan Razavi Organization, Mashhad, Iran

    Hossein Aghajani

  2. Department of Tourism Management and Planning, Institute of Tourism Research Academic Center for Education, Culture and Research, Khorasan Razavi Organization, Mashhad, Iran

    Ali Alizadeh-Zoeram

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  1. Hossein Aghajani
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  2. Ali Alizadeh-Zoeram
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Correspondence to Ali Alizadeh-Zoeram.

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Aghajani, H., Alizadeh-Zoeram, A. Identifying urban neighborhoods with higher potential for social investment using GIS-FIS approach. Appl Geomat 13, 1–13 (2021). https://doi.org/10.1007/s12518-020-00317-4

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  • DOI: https://doi.org/10.1007/s12518-020-00317-4

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