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Optimal school site selection in Urban areas using deep neural networks

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Abstract

Finding an optimal location for a future facility amidst existing sites is a challenging task—and potentially has numerous applications in resource planning. Formally, given a set of candidate sites S and existing sites E, the Optimal Site Selection (OSS) problem aims to find the optimal location \(s \in S\) that serves maximum customers. In this paper, we study the OSS problem for predicting school location from a set of available sites in the Urban areas. We leverage deep neural networks to solve this problem in a resource and time-efficient way—using a dataset of 47,926 schools across 36 districts of the Punjab province. The dataset includes numerous features such as no. of teachers, no. of classrooms, and no. of sections associated with each school site. We also incorporate a novel feature called site influence that gives the expected number of potential students for a particular school. Our proposed model performs exceedingly well with the addition of this feature. We approach this problem by defining class labels based on a given school’s capacity—and further train a deep neural network using the aforementioned features to achieve accuracy values up to 82%. Overall, this study facilitates optimal school site selection in the Urban areas, thereby, adding to communal prosperity and growth.

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References

  • Achtert E, Böhm C, Kröger P, Kunath P, Pryakhin A, Renz M (2006) Efficient reverse k-nearest neighbor search in arbitrary metric spaces. In: Proceedings of the 2006 ACM SIGMOD International Conference on Management of Data, SIGMOD ’06, pages 515–526, New York, NY, USA. ACM

  • Achtert E, Kriegel H-P, Kröger P, Renz M, Züfle A (2009) Reverse k-nearest neighbor search in dynamic and general metric databases. In: Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology, EDBT ’09, pages 886–897, New York, NY, USA. ACM

  • Ahmed BH, Ghabayen AS (2020) Review rating prediction framework using deep learning. J Ambient Intell Hum Comput 2:1–10

    Google Scholar 

  • Aksenova SS, Zhang D, Lu M (2006) Enrollment prediction through data mining. In: 2006 IEEE International Conference on Information Reuse Integration, pages 510–515

  • Bamrah IS, Girdhar A (2015) Investigation on impact of reservation policy on student enrollment using data mining. In: 2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), pages 1–5

  • Borah MD, Jindal R, Gupta D, Deka GC (2011) Application of knowledge based decision technique to predict student enrollment decision. In: 2011 International Conference on Recent Trends in Information Systems, pages 180–184

  • Cabello S, Díaz-Báñez JM, Langerman S, Seara C, Ventura I (2005) Reverse facility location problems. In: Conference on Computational Geometry

  • Castillo VH, Equigua LS, Álvarez JL, Medina DAM (2013) Projecting school enrolments through an integral flow model. In: 2013 8th Iberian Conference on Information Systems and Technologies (CISTI), pages 1–4

  • Cheema MA, Zhang W, Lin X, Zhang Y (2012) Efficiently processing snapshot and continuous reverse k nearest neighbors queries. VLDB J 21(5):703–728

    Article  Google Scholar 

  • Cui J, Wang M, Li H, Cai Y (2018) Place your next branch with mile-run: Min-dist location selection over user movement. Inf Sci 463–464:1–20

    MathSciNet  Google Scholar 

  • Ding X, Zhang Y, Chen L, Gao Y, Zheng B (2018) Distributed k-nearest neighbor queries in metric spaces. In: Cai Y, Ishikawa Y, Xu J (eds) Web and big data. Springer International Publishing, Cham, pp 236–252

    Chapter  Google Scholar 

  • Du Y, Zhang D, Xia T (2005) The optimal-location query. In: Bauzer Medeiros C, Egenhofer MJ, Bertino E (eds) Advances in spatial and temporal databases. Springer, Berlin, pp 163–180

    Chapter  Google Scholar 

  • Emrich T, Kriegel H-P, Kröger P, Niedermayer J, Renz M, Züfle A (2015) On reverse-k-nearest-neighbor joins. GeoInformatica 19(2):299–330

    Article  Google Scholar 

  • Ferreira KM, de Queiroz TA (2018) Two effective simulated annealing algorithms for the location-routing problem. Appl Soft Comput 70:389–422

    Article  Google Scholar 

  • Fortune S (1987) A sweepline algorithm for voronoi diagrams. Algorithmica 2(1):153

    Article  MathSciNet  Google Scholar 

  • Gao Y, Qi S, Chen L, Zheng B, Li X (2015) On efficient k-optimal-location-selection query processing in metric spaces. Inf Sci 298(C):98–117

    Article  MathSciNet  Google Scholar 

  • Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT Press, Cambridge

    MATH  Google Scholar 

  • Grip RS, Grip ML (2019) Using multiple methods to provide prediction bands of k-12 enrollment projections. Population Research and Policy Review

  • Guohui L, Yanhong L, Jianjun L, Shu L, Fumin Y (2010) Continuous reverse k nearest neighbor monitoring on moving objects in road networks. Inf Syst 35(8):860–883

    Article  Google Scholar 

  • Huang J, Wen Z, Qi J, Zhang R, Chen J, He Z (2011) Top-k most influential locations selection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, CIKM ’11, pages 2377–2380, New York, NY, USA. ACM

  • Huang J-S, Chen B-Q, Zeng N-Y, Cao X-C, Li Y (2020) Accurate classification of ecg arrhythmia using mowpt enhanced fast compression deep learning networks. Journal of Ambient Intelligence and Humanized Computing. page inpress

  • Karatas M, Yakıcı E (2018) An iterative solution approach to a multi-objective facility location problem. Appl Soft Comput 62:272–287

    Article  Google Scholar 

  • Korn F, Muthukrishnan S, Muthukrishnan S (2000) Influence sets based on reverse nearest neighbor queries. SIGMOD Rec 29(2):201–212

    Article  Google Scholar 

  • Lai C-M (2019) Integrating simplified swarm optimization with ahp for solving capacitated military logistic depot location problem. Appl Soft Comput 78:1–12

    Article  Google Scholar 

  • Lai C-M, Chiu C-C, Liu W-C, Yeh W-C (2019) A novel nondominated sorting simplified swarm optimization for multi-stage capacitated facility location problems with multiple quantitative and qualitative objectives. Appl Soft Comput 84:105684

    Article  Google Scholar 

  • Li S (2013) A 1.488 approximation algorithm for the uncapacitated facility location problem. Information and Computation 222:45–58. 38th International Colloquium on Automata, Languages and Programming (ICALP 2011)

  • Li D, Li H, Wang M, Cui J (2019) k-collective influential facility placement over moving object. In: 2019 20th IEEE International Conference on Mobile Data Management (MDM), pages 191–200

  • Liu J, Liu J (2019) Applying multi-objective ant colony optimization algorithm for solving the unequal area facility layout problems. Appl Soft Comput 74:167–189

    Article  Google Scholar 

  • Lu J, Lu Y, Cong G (2011) Reverse spatial and textual k nearest neighbor search. In: Proceedings of the 2011 ACM SIGMOD International Conference on Management of data, pages 349–360

  • Mitra S, Saraf P, Sharma R, Bhattacharya A, Ranu S (2019) Netclus: A scalable framework to mine top-k locations for placement of trajectory-aware services. In: Proceedings of the ACM India Joint International Conference on Data Science and Management of Data, CoDS-COMAD ’19, pages 27–35, New York, NY, USA. ACM

  • Pekel E, Kara SS (2019) Solving fuzzy capacitated location routing problem using hybrid variable neighborhood search and evolutionary local search. Appl Soft Comput 83:105665

    Article  Google Scholar 

  • Prakash S, Sangeetha K (2020) Deep multilayer and nonlinear kernelized lasso feature learning for healthcare in big data environment. J Ambient Intell Hum Comput 2:2

    Google Scholar 

  • Qi J, Zhang R, Kulik L, Lin D, Xue Y (2012) The min-dist location selection query. In: 2012 IEEE 28th International Conference on Data Engineering, pages 366–377

  • Qi J, Zhang R, Wang Y, Xue AY, Yu G, Kulik L (2014) The min-dist location selection and facility replacement queries. World Wide Web 17(6):1261–1293

    Article  Google Scholar 

  • Shang S, Yuan B, Deng K, Xie K, Zhou X (2011) Finding the most accessible locations: reverse path nearest neighbor query in road networks. In: Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, GIS ’11, pages 181–190, New York, NY, USA. ACM

  • Slim A, Hush D, Ojha T, Babbitt T (2018) Predicting student enrollment based on student and college characteristics. In: EDM

  • Stallings R, Samanta B (2014) Prediction of university enrollment using computational intelligence. In: 2014 IEEE Symposium on Swarm Intelligence, pages 1–8

  • Stanoi I, Riedewald M, Agrawal D, Abbadi AE (2001) Discovery of influence sets in frequently updated databases. In: Proceedings of the 27th International Conference on Very Large Data Bases, VLDB ’01, page 99–108, San Francisco, CA, USA. Morgan Kaufmann Publishers Inc

  • Sun Y, Zhang R, Xue AY, Qi J, Du X (2016) Reverse nearest neighbor heat maps: A tool for influence exploration. In: 2016 IEEE 32nd International Conference on Data Engineering (ICDE), pages 966–977

  • Wang H, Wang H, Guo J, Feng H (2014) A fuzzy time series forecasting model based on yearly difference of the student enrollment number. In: 2nd International Conference on Soft Computing in Information Communication Technology. Atlantis Press

  • Wang M, Li H, Cui J, Deng K, Bhowmick SS, Dong Z (2017) Pinocchio: Probabilistic influence-based location selection over moving objects. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pages 21–22

  • Wang Z, Wan Q, Qin Y, Fan S, Xiao Z (2020) Research on intelligent algorithm for alerting vehicle impact based on multi-agent deep reinforcement learning. Journal of Ambient Intelligence and Humanized Computing 1–11

  • Weber A, Friedrich CJ (1929) Theory of the location of industries. University of Chicago Press, Reading

    Google Scholar 

  • Williamson DP, Shmoys DB (2011) The design of approximation algorithms, 1st edn. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Wong RC-W, Özsu MT, Yu PS, Fu AW-C, Liu L (2009) Efficient method for maximizing bichromatic reverse nearest neighbor. Proc. VLDB Endow 2(1):1126–1137

    Article  Google Scholar 

  • Wong RC-W, Özsu MT, Fu AW-C, Yu PS, Liu L, Liu Y (2011) Maximizing bichromatic reverse nearest neighbor for lp-norm in two- and three-dimensional spaces. VLDB J 20(6):893–919

    Article  Google Scholar 

  • Xia T, Zhang D, Kanoulas E, Du Y (2005) On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases, VLDB ’05, pages 946–957. VLDB Endowment

  • Xiao X, Yao B, Li F (2011) Optimal location queries in road network databases. In: Proceedings of the 2011 IEEE 27th International Conference on Data Engineering, ICDE ’11, pages 804–815, Washington, DC, USA. IEEE Computer Society

  • Xu C, Gu Y, Zimmermann R, Lin S, Yu G (2013) Group location selection queries over uncertain objects. IEEE Trans Knowl Data Eng 25(12):2796–2808

    Article  Google Scholar 

  • Yamunadevi M, Ranjani SS (2020) Efficient segmentation of the lung carcinoma by adaptive fuzzy-glcm (af-glcm) with deep learning based classification. Journal of Ambient Intelligence and Humanized Computing, page inpress

  • Yan D, Wong RC-W, Ng W (2011) Efficient methods for finding influential locations with adaptive grids. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, CIKM ’11, pages 1475–1484, New York, NY, USA. ACM

  • Yang S, Cheema MA, Lin X, Zhang Y (2014) Slice: reviving regions-based pruning for reverse k nearest neighbors queries. In: 2014 IEEE 30th International Conference on Data Engineering, pages 760–771. IEEE

  • Yang S, Cheema MA, Lin X, Wang W (2015) Reverse k nearest neighbors query processing: experiments and analysis. Proc. VLDB Endow 8(5):605–616

    Article  Google Scholar 

  • Yang S, Cheema MA, Lin X, Zhang Y, Zhang W (2017) Reverse k nearest neighbors queries and spatial reverse top-k queries. VLDB J 26(2):151–176

    Article  Google Scholar 

  • Yilmaz E, Elbasi S, Ferhatosmanoglu H (2017) Predicting optimal facility location without customer locations. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’17, pages 2121–2130, New York, NY, USA. ACM

  • Zhang D, Du Y, Xia T, Tao Y (2006) Progressive computation of the min-dist optimal-location query. In: Proceedings of the 32Nd International Conference on Very Large Data Bases, VLDB ’06, pages 643–654. VLDB Endowment

  • Zhu J, Liu W, Liu Y, Wang D, Qu S, Duan Y, Yao J (2020) Smart city oriented optimization of residential blocks on intensive urban sensing data based on fuzzy evaluation algorithm. Journal of Ambient Intelligence and Humanized Computing. page inpress

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Acknowledgements

This work is partially supported by the Higher Education Commission (HEC), Pakistan under the National Center for Big Data and Cloud Computing funding for the Crime Investigation and Prevention Lab (CIPL) project at Information Technology University, Lahore. We are thankful to Punjab Information Technology Board (PITB) for providing us with the dataset for this study.

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  1. Information Technology University, 346-B, Ferozepur Road, Lahore, Pakistan

    Nimra Zaheer, Saeed-Ul Hassan, Mohsen Ali & Mudassir Shabbir

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  1. Nimra Zaheer
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  2. Saeed-Ul Hassan
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  4. Mudassir Shabbir
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Correspondence to Nimra Zaheer.

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Zaheer, N., Hassan, SU., Ali, M. et al. Optimal school site selection in Urban areas using deep neural networks. J Ambient Intell Human Comput 13, 313–327 (2022). https://doi.org/10.1007/s12652-021-02903-9

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