Social Indicators Research

, Volume 142, Issue 3, pp 1075–1102 | Cite as

Application of Big Data and Analytic Network Process for the Adaptive Reuse Strategies of School Land

  • Jhong-You Huang
  • Wann-Ming WeyEmail author


Most recent discussion of the adaptive reuse of school land has focused almost exclusively on repurposing or redeploying vacant school space rather than comprehensively re-planning and constructing the entire school land for the overall needs of society and urban development. The relevant government agencies for school land reuse in Taiwan, such as the Ministry of Education and municipal governments, mostly provide subjective regulations or revitalization provisions for the sustainable development of school resources; however, no specific scientific assessment or a planning procedure has been proposed to revitalize school land. Therefore, constructing a scientific, quantitative, and objective planning framework and procedure is necessary for the adaptive reuse of school land based on the needs of overall society and urban development in order to replace the existing and outdated planning philosophy and to correct prominent shortcomings of past planning operations that were solely in accordance with the qualitative judgment and decision making of official agencies. In this study, we mainly adopted the analytic network process (ANP) and big data, including demographics, facility usage, and social welfare indicators, to assist the Taipei City government to construct or reform land reuse strategies for junior high and elementary schools facing immediate or future closure, consolidation, or downsizing. To take a more realistic approach to improve final decision making, the investigation of expert questionnaires through the ANP was based on the consideration of future trends that were objectively evaluated by big datasets. The novel planning philosophy and concise decision framework for reuse strategies we designed are expected to improve public decision-making transparency, adaptive reuse effectiveness, and quality of urban life. Ultimately, our proposed strategies and suggestions can not only assist local public sectors to promote the policy of adaptive reuse of surplus school lands but also serve as an appropriate blueprint of urban sustainability for the central government in the near future.


Reuse of school land Analytic network process Big data Quality of urban life 



The authors would like to thank anonymous referees and the Editor of the journal for constructive comments and suggestions. The authors also wish to acknowledge Jun-Hui Xie for his participation in data collection and analysis activities. Finally, the authors would like to express their appreciations to Ministry of Science and Technology (MOST) of Taiwan and National Taipei University (NTPU) for the support of the projects: MOST 104-2410-H-305-077-MY3, MOST 106-2811-H-305-004, and 107-NTPU_A-H&E-143-001. The views expressed are those of the authors and do not represent the official policy or positions of the MOST and NTPU.


  1. Aravot, I. (1996). Integration of future users’ evaluations into the process of urban revitalization. Evaluation and Program Planning, 19(1), 65–78.Google Scholar
  2. Asosheh, A., Nalchigar, S., & Jamporazmey, M. (2010). Information technology project evaluation: An integrated data envelopment analysis and balanced scorecard approach. Expert Systems with Applications, 37(8), 5931–5938.Google Scholar
  3. Badri, M. A., Davis, D., & Davis, D. (2001). A comprehensive 0–1 goal programming model for project selection. International Journal of Project Management, 19(4), 243–252.Google Scholar
  4. Batty, M. (2012). Building a science of cities. Cities, 29, S9–S16.Google Scholar
  5. Batty, M. (2013). Big data, smart cities and city planning. Dialogues in Human Geography, 3(3), 274–279.Google Scholar
  6. Biørn, E. (2017). Econometrics of Panel Data: Methods and Applications. Oxford: Oxford University Press.Google Scholar
  7. Börjeson, L., Höjer, M., Dreborg, K.-H., Ekvall, T., & Finnveden, G. (2006). Scenario types and techniques: Towards a user’s guide. Futures, 38(7), 723–739.Google Scholar
  8. Bullen, P. A., & Love, P. E. (2010). The rhetoric of adaptive reuse or reality of demolition: Views from the field. Cities, 27(4), 215–224.Google Scholar
  9. Buttoud, G. (2000). How can policy take into consideration the “full value” of forests? Land Use Policy, 17(3), 169–175.Google Scholar
  10. Çevik, S., Vural, S., Tavşan, F., & Aşık, Ö. (2008). An example to renovation–revitalization works in historical city centres: Kunduracılar Street/Trabzon-Turkey. Building and Environment, 43(5), 950–962.Google Scholar
  11. Chang, Y.-H., Wey, W.-M., & Tseng, H.-Y. (2009). Using ANP priorities with goal programming for revitalization strategies in historic transport: A case study of the Alishan Forest Railway. Expert Systems with Applications, 36(4), 8682–8690.Google Scholar
  12. Chapple, K., Jackson, S., & Martin, A. J. (2010). Concentrating creativity: The planning of formal and informal arts districts. City, Culture and Society, 1(4), 225–234.Google Scholar
  13. Cho, K.-T., & Kwon, C.-S. (2004). Hierarchies with dependence of technological alternatives: A cross-impact hierarchy process. European Journal of Operational Research, 156(2), 420–432.Google Scholar
  14. Couch, C., & Dennemann, A. (2000). Urban regeneration and sustainable development in Britain: The example of the Liverpool Ropewalks Partnership. Cities, 17(2), 137–147.Google Scholar
  15. Deng, J. (1989). Introduction to grey system theory. The Journal of Grey System, 1, 1–24.Google Scholar
  16. Deng, J. (2010). Introduction to grey mathematical resource science. Wuhan: Huazhong University of Science & Technology Press.Google Scholar
  17. Dutta, M., Banerjee, S., & Husain, Z. (2007). Untapped demand for heritage: A contingent valuation study of Prinsep Ghat. Calcutta. Tourism Management, 28(1), 83–95.Google Scholar
  18. Floridi, L. (2012). Big data and their epistemological challenge. Philosophy & Technology, 25(4), 435–437.Google Scholar
  19. Francesco, T., & Pierluigi, M. (2017). Evaluation of vacant and redundant public properties and risk control: A model for the definition of the optimal mix of eligible functions. Journal of Property Investment & Finance, 35(1), 75–100.Google Scholar
  20. Fraser, E. D. G., Dougill, A. J., Mabee, W. E., Reed, M., & McAlpine, P. (2006). Bottom up and top down: Analysis of participatory processes for sustainability indicator identification as a pathway to community empowerment and sustainable environmental management. Journal of Environmental Management, 78(2), 114–127.Google Scholar
  21. Helleman, G., & Wassenberg, F. (2004). The renewal of what was tomorrow’s idealistic city. Amsterdam’s Bijlmermeer high-rise. Cities, 21(1), 3–17.Google Scholar
  22. Janssen, M., & Kuk, G. (2016). The challenges and limits of big data algorithms in technocratic governance. Government Information Quarterly, 33(3), 371–377.Google Scholar
  23. Kang, J., & Zhao, H. (2012). Application of improved grey model in long-term load forecasting of power engineering. Systems Engineering Procedia, 3, 85–91.Google Scholar
  24. Kayacan, E., Ulutas, B., & Kaynak, O. (2010). Grey system theory-based models in time series prediction. Expert Systems with Applications, 37(2), 1784–1789.Google Scholar
  25. Kitchin, R. (2013). Big data and human geography opportunities, challenges and risks. Dialogues in human geography, 3(3), 262–267.Google Scholar
  26. Kitchin, R. (2014). The real-time city? Big data and smart urbanism. GeoJournal, 79(1), 1–14.Google Scholar
  27. Kitchin, R., Lauriault, T. P., & McArdle, G. (2015). Knowing and governing cities through urban indicators, city benchmarking and real-time dashboards. Regional Studies, Regional Science, 2(1), 6–28.Google Scholar
  28. Kleine, A. (2004). A general model framework for DEA. Omega, 32(1), 17–23.Google Scholar
  29. Kuo, J. K., Kuo, C. J., & Cheng, H. Y. (2007). A study of developing rural tourism as a strategy for local revitalization. Journal of Tourism Studies, 13(1), 23–45. (In Chinese).Google Scholar
  30. Kyparisis, G. J., Gupta, S. K., & Ip, C.-M. (1996). Project selection with discounted returns and multiple constraints. European Journal of Operational Research, 94(1), 87–96.Google Scholar
  31. Lai, S.-K., & Huang, J.-Y. (2017). Theoretical foundation of a decision network for urban development. Frontiers of Information Technology & Electronic Engineering, 18(8), 1033–1039.Google Scholar
  32. Lamb, M., & Gregory, M. (1997). Industrial concerns in technology selection. In Proceedings of the Portland international conference on management of engineering and technology.Google Scholar
  33. Langston, C., Wong, F. K., Hui, E. C., & Shen, L. Y. (2009). Strategic assessment of building adaptive reuse opportunities in Hong Kong. Building and Environment, 43(10), 1709–1718.Google Scholar
  34. Lin, Y.-H., Tsai, K.-M., Shiang, W.-J., Kuo, T.-C., & Tsai, C.-H. (2009). Research on using ANP to establish a performance assessment model for business intelligence systems. Expert Systems with Applications, 36(2), 4135–4146.Google Scholar
  35. Liu, S., & Lin, Y. (2006). Grey information: Theory and practical applications. London: Springer.Google Scholar
  36. Liu, S., & Lin, Y. (2010). Grey systems: Theory and applications. Berlin: Springer.Google Scholar
  37. Liu, X., Peng, H., Bai, Y., Zhu, Y., & Liao, L. (2014). Tourism flows prediction based on an improved grey GM(1,1) Model. Procedia - Social and Behavioral Sciences, 138, 767–775.Google Scholar
  38. Liu, S., Yang, Y., & Forrest, J. (2017). Grey data analysis: Methods, models and applications. Singapore: Springer.Google Scholar
  39. Maclaren, V. W. (1996). Urban Sustainability Reporting. Journal of the American Planning Association, 62(2), 184–202.Google Scholar
  40. Martin, D. (1998). Automatic neighbourhood identification from population surfaces. Computers, Environment and Urban Systems, 22(2), 107–120.Google Scholar
  41. Martins, M. J. F., Puckett, T. M., Lockwood, R., Swaddle, J. P., & Hunt, G. (2018). High male sexual investment as a driver of extinction in fossil ostracods. Nature, 556(7701), 366–369.Google Scholar
  42. McCool, S. F., & Martin, S. R. (1994). Community attachment and attitudes toward tourism development. Journal of Travel Research, 32(3), 29–34.Google Scholar
  43. Mese, M. (1998). Rural community revitalization by using regional resources and development of participatory planning method (SS method). Okayama University Scientific Reports of the Faculty of Agriculture, 87, 215–225. (In Japanese).Google Scholar
  44. Michael, H. (2009). Policy analytical capacity and evidence-based policy-making: Lessons from Canada. Canadian Public Administration, 52(2), 153–175.Google Scholar
  45. Nilsson, M., & Dalkmann, H. (2001). Decision making and strategic environmental assessment. Journal of Environmental Assessment Policy and Management, 03(03), 305–327.Google Scholar
  46. Olson, D. L. (2004). Comparison of weights in TOPSIS models. Mathematical and Computer Modelling, 40(7), 721–727.Google Scholar
  47. Phipps, A. G. (2008). Reuses of closed schools in Windsor, Ontario. Socio-Economic Planning Sciences, 42(1), 18–30.Google Scholar
  48. Raco, M. (2003). Assessing the discourses and practices of urban regeneration in a growing region. Geoforum, 34(1), 37–55.Google Scholar
  49. Razzu, G. (2005). Urban redevelopment, cultural heritage, poverty and redistribution: the case of Old Accra and Adawso House. Habitat International, 29(3), 399–419.Google Scholar
  50. Saaty, T. L. (1980). The analytic hierarchy process. New York: McGraw-Hill.Google Scholar
  51. Saaty, T. L. (1996). Decision making with dependence and feedback: The analytic network process. Pittsburgh: RWS Publications.Google Scholar
  52. Saaty, T. L. (2005). Theory and applications of the analytic network process: Decision making with benefits, opportunities, costs, and risks. Pittsburgh: RWS publications.Google Scholar
  53. Saaty, T. L. (2007). Time dependent decision-making; dynamic priorities in the AHP/ANP: Generalizing from points to functions and from real to complex variables. Mathematical and Computer Modelling, 46(7), 860–891.Google Scholar
  54. Saaty, T. L. (2008). Decision making with the analytic hierarchy process. International Journal of Services Sciences, 1(1), 83–98.Google Scholar
  55. Saaty, T. L. (2013). Analytic network process. In S. I. Gass & M. C. Fu (Eds.), Encyclopedia of operations research and management science (pp. 64–72). Boston: Springer.Google Scholar
  56. Saaty, T. L., & Ozdemir, M. (2003). Negative priorities in the analytic hierarchy process. Mathematical and Computer Modelling, 37(9), 1063–1075.Google Scholar
  57. Saaty, T. L., & Sodenkamp, M. (2010). The analytic hierarchy and analytic network measurement processes: The measurement of intangibles. In C. Zopounidis & P. M. Pardalos (Eds.), Handbook of multicriteria analysis (pp. 91–166). Berlin: Springer.Google Scholar
  58. Saaty, T. L., & Takizawa, M. (1986). Dependence and independence: From linear hierarchies to nonlinear networks. European Journal of Operational Research, 26(2), 229–237.Google Scholar
  59. Sairinen, R., & Kumpulainen, S. (2006). Assessing social impacts in urban waterfront regeneration. Environmental Impact Assessment Review, 26(1), 120–135.Google Scholar
  60. Steigenberger, N., Lübcke, T., Fiala, H., & Riebschläger, A. (2017). Decision modes in complex task environments. Boca Raton: CRC Press.Google Scholar
  61. Turkoglu, H. (2015). sustainable development and quality of urban life. Procedia - Social and Behavioral Sciences, 202, 10–14.Google Scholar
  62. Tversky, A., & Kahneman, D. (1981). The framing of decisions and the psychology of choice. Science, 211(4481), 453–458.Google Scholar
  63. Ünlü, A. (2010). Urban regeneration, renewal or rehabilitation what for and for whom? Open House International, 35(4), 51–57.Google Scholar
  64. Wagner, F. W., Joder, T. E., & Mumphrey, A. J. (Eds.). (1995). Urban revitalization: Policies and programs. Thousand Oaks: Sage Publications.Google Scholar
  65. Wang, W.-M., Lee, A. H. I., & Chang, D.-T. (2010). An integrated FDM–ANP evaluation model for sustainable development of housing community. Optimization Letters, 4(2), 239–257.Google Scholar
  66. Wang, W. M. & Wu, Z. L. (2008). A Preliminary Exploration for the assessment modeling of revitalizing and regenerating alternatives on the region with historical heritage. In Proceedings of the 2008 conference on science technology & society (In Chinese).Google Scholar
  67. Wedding, G. C., & Crawford-Brown, D. (2007). Measuring site-level success in brownfield redevelopments: A focus on sustainability and green building. Journal of Environmental Management, 85(2), 483–495.Google Scholar
  68. West, G. (2017). Scale: The universal laws of growth, innovation, sustainability, and the pace of life in organisms, cities, economies, and companies. New York: Penguin Press.Google Scholar
  69. Wey, W.-M., & Wu, K.-Y. (2007). Using ANP priorities with goal programming in resource allocation in transportation. Mathematical and Computer Modelling, 46(7), 985–1000.Google Scholar
  70. Wijnmalen, D. J. D. (2007). Analysis of benefits, opportunities, costs, and risks (BOCR) with the AHP–ANP: A critical validation. Mathematical and Computer Modelling, 46(7), 892–905.Google Scholar
  71. Yang, M. C. (2002). Local cultural industry and local revitalization, unpublished doctoral dissertation, Graduate Institute of Urban Planning in National Taipei University, Taipei. (In Chinese)Google Scholar
  72. Yu, J.-H., & Kwon, H.-R. (2011). Critical success factors for urban regeneration projects in Korea. International Journal of Project Management, 29(7), 889–899.Google Scholar
  73. Zoffer, J., Bahurmoz, A., Hamid, M. K., Minutolo, M., & Saaty, T. (2008). Synthesis of complex criteria decision making: A case towards a consensus agreement for a middle east conflict resolution. Group Decision and Negotiation, 17(5), 363–385.Google Scholar

Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  1. 1.Department of Real Estate and Built EnvironmentNational Taipei UniversityNew Taipei CityTaiwan

Personalised recommendations