Geographically and temporally weighted co-location quotient: an analysis of spatiotemporal crime patterns in greater Manchester

References

• Anderson, T.K., 2009. Kernel density estimation and K-means clustering to profile road accident hotspots. Accident; Analysis and Prevention, 41 (3), 359–364.
   PubMed Web of Science ®Google Scholar
• Ansari, M.Y., et al., 2019. Spatiotemporal clustering: a review. Artificial Intelligence Review, 53 (4), 2381–2423.
   Web of Science ®Google Scholar
• Atluri, G., Karpatne, A., and Kumar, V., 2019. Spatio-temporal data mining. ACM Computing Surveys, 51 (4), 1–41.
   Web of Science ®Google Scholar
• Bannister, J., and O'Sullivan, A., 2021. Big data in the city. Urban Studies, 58 (15), 3061–3070.
   Web of Science ®Google Scholar
• Batty and Michael 2016. Big data and the city. Built Environment, 42 (3), 321–337.
   Google Scholar
• Brunsdon, C., 1995. Estimating probability surfaces for geographical point data: An adaptive kernel algorithm. Computers & Geosciences, 21 (7), 877–894.
   Web of Science ®Google Scholar
• Brunsdon, C., Fotheringham, A.S., and Charlton, M.E., 2010. Geographically weighted regression: a method for exploring spatial nonstationarity. Geographical Analysis, 28 (4), 281–298.
   Web of Science ®Google Scholar
• Cao, W., et al., 2017. Location patterns of urban industry in Shanghai and implications for sustainability. Journal of Geographical Sciences, 27 (7), 857–878.
   Web of Science ®Google Scholar
• Cheng, T., and Adepeju, M., 2014. Modifiable temporal unit problem (MTUP) and its effect on space-time cluster detection. PLoS One, 9 (6), e100465.
   PubMed Web of Science ®Google Scholar
• Cho, S.-H., Lambert, D.M., and Chen, Z., 2010. Geographically weighted regression bandwidth selection and spatial autocorrelation: an empirical example using Chinese agriculture data. Applied Economics Letters, 17 (8), 767–772.
   Web of Science ®Google Scholar
• Cleveland, W.S., 1979. Robust locally weighted regression and smoothing scatterplots. Journal of the American Statistical Association, 74 (368), 829–836.
   Web of Science ®Google Scholar
• Corcoran, J., et al., 2021. The temporality of place: Constructing a temporal typology of crime in commercial precincts. Environment and Planning B: Urban Analytics and City Science, 48 (1), 9–24.
   Google Scholar
• Cromley, R.G., Hanink, D.M., and Bentley, G.C., 2014. Geographically weighted colocation quotients: specification and application. The Professional Geographer, 66 (1), 138–148.
   Web of Science ®Google Scholar
• Dubé, J., and Legros, D., 2012. A spatio-temporal measure of spatial dependence: An example using real estate data. Papers in Regional Science, 92, 19–30.
   Web of Science ®Google Scholar
• Dubé, J., and Legros, D., 2013. Dealing with spatial data pooled over time in statistical models. Letters in Spatial and Resource Sciences, 6 (1), 1–18.
   Google Scholar
• Dubé, J., and Legros, D., 2015. Modeling spatial data pooled over time: schematic representation and monte carlo evidences. Theoretical Economics Letters, 05 (01), 132–154.
   Google Scholar
• Dubé, J., Legros, D., and Thanos, S., 2018. Past price ‘memory’ in the housing market: testing the performance of different spatio-temporal specifications. Spatial Economic Analysis, 13 (1), 118–138.
   Web of Science ®Google Scholar
• Dubin, R., Pace, R.K., and Thibodeau, T.G., 1999. Spatial autoregression techniques for real estate data. Journal of Real Estate Literature, 7 (1), 79–96.
   Google Scholar
• Eckardt, M., and Mateu, J., 2021. Partial and semi‐partial statistics of spatial associations for multivariate areal data. Geographical Analysis, 53 (4), 818–835.
   Web of Science ®Google Scholar
• Emani, C.K., Cullot, N., and Nicolle, C., 2015. Understandable big data: a survey. Computer Science Review, 17 (AUG), 70–81.
   Google Scholar
• Ferreira, L.N., et al., 2020. Spatiotemporal data analysis with chronological networks. Nature Communications, 11 (1), 4036.
   Web of Science ®Google Scholar
• Fotheringham, A.S., Brunsdon, C.F., and Charlton, M.E., 2002. Geographically Weighted Regression: The Analysis of Spatially Varying Relationships.
   Google Scholar
• Fotheringham, A.S., Crespo, R., and Yao, J., 2015. Geographical and temporal weighted regression (GTWR). Geographical Analysis, 47 (4), 431–452.
   Web of Science ®Google Scholar
• Fotheringham, A.S., and Oshan, T.M., 2016. Geographically weighted regression and multicollinearity: dispelling the myth. Journal of Geographical Systems, 18 (4), 303–329.
   Web of Science ®Google Scholar
• Hamada, N., et al., 2015., Population Synthesis via k-Nearest Neighbor Crossover Kernel. ed. 2015 IEEE International Conference on Data Mining (ICDM),
   Google Scholar
• Harris, P., et al., 2015. Enhancements to a geographically weighted principal component analysis in the context of an application to an environmental data set. Geographical Analysis, 47 (2), 146–172.
   Web of Science ®Google Scholar
• Harris, R., et al., 2010. Grid-enabling geographically weighted regression: a case study of participation in higher education in England. Transactions in GIS, 14 (1), 43–61.
   Web of Science ®Google Scholar
• Helbich, M., and Leitner, M., 2017. Frontiers in spatial and spatiotemporal crime analytics—an editorial. ISPRS International Journal of Geo-Information, 6 (3), 73.
   Web of Science ®Google Scholar
• Hu, Y., Zhang, Y., and Shelton, K.S., 2018. Where are the dangerous intersections for pedestrians and cyclists: A colocation-based approach. Transportation Research Part C: Emerging Technologies, 95, 431–441.
   Web of Science ®Google Scholar
• Huang, B., Wu, B., and Barry, M., 2010. Geographically and temporally weighted regression for modeling spatio-temporal variation in house prices. International Journal of Geographical Information Science, 24 (3), 383–401.
   Web of Science ®Google Scholar
• Jefferson, B.J., 2018. Predictable policing: predictive crime mapping and geographies of policing and race. Annals of the American Association of Geographers, 108 (1), 1–16.
   Web of Science ®Google Scholar
• Kandt, J., and Batty, M., 2021. Smart cities, big data and urban policy: towards urban analytics for the long run. Cities, 109, 102992.
   Web of Science ®Google Scholar
• Kharrazi, A., Qin, H., and Zhang, Y., 2016. Urban big data and sustainable development goals: challenges and opportunities. Sustainability, 8 (12), 1293.
   Web of Science ®Google Scholar
• Lee, J., and Li, S., 2017. Extending Moran's Index for measuring spatiotemporal clustering of geographic events. Geographical Analysis, 49 (1), 36–57.
   Web of Science ®Google Scholar
• Li, Z., Cheng, J., and Wu, Q., 2016. Analyzing regional economic development patterns in a fast developing province of China through geographically weighted principal component analysis. Letters in Spatial and Resource Sciences, 9 (3), 1–13.
   Google Scholar
• Liu, J., et al., 2019. Pedestrian injury severity in motor vehicle crashes: an integrated spatio-temporal modeling approach. Accident; Analysis and Prevention, 132, 105272
   PubMed Web of Science ®Google Scholar
• Liu, J., et al., 2020. Urban big data fusion based on deep learning: An overview. Information Fusion, 53, 123–133.
   Web of Science ®Google Scholar
• Moura, J., et al., 2020. On the design and analysis of structured-ANN for online PID-tuning to bulk resumption process in ore mining system. Neurocomputing, 402, 266–282.
   Web of Science ®Google Scholar
• Myllymäki, M., Kuronen, M., and Mrkvička, T., 2021. Testing global and local dependence of point patterns on covariates in parametric models. Spatial Statistics, 42, 100436.
   Web of Science ®Google Scholar
• Nakaya, T., et al., 2005. Geographically weighted Poisson regression for disease association mapping. Statistics in Medicine, 24 (17), 2695–2717.
   PubMed Web of Science ®Google Scholar
• Olden, J., and Neff, B., 2001. Cross-correlation bias in lag analysis of aquatic time series. Marine Biology, 138 (5), 1063–1070.
   Web of Science ®Google Scholar
• Piza, E.L., and Carter, J.G., 2018. Predicting initiator and near repeat events in spatiotemporal crime patterns: an analysis of residential burglary and motor vehicle theft. Justice Quarterly, 35 (5), 842–829.
   Web of Science ®Google Scholar
• Pope, M., and Song, W., 2015. Spatial relationship and colocation of crimes in Jefferson County, Kentucky. Papers in Applied Geography, 1 (3), 243–248.
   Google Scholar
• Quick, M., Law, J., and Li, G., 2019. Time-varying relationships between land use and crime: A spatio-temporal analysis of small-area seasonal property crime trends. Environment and Planning B: Urban Analytics and City Science, 46 (6), 1018–1035.
   Google Scholar
• Ren, T., et al., 2014. Moran's I test of spatial panel data model — Based on bootstrap method. Economic Modelling, 41, 9–14.
   Web of Science ®Google Scholar
• Shen, C., Li, C., and Si, Y., 2016. Spatio-temporal autocorrelation measures for nonstationary series: A new temporally detrended spatio-temporal Moran's index. Physics Letters A, 380 (1-2), 106–116.
   Web of Science ®Google Scholar
• Spettl, A., et al., 2015. Stochastic 3D modeling of Ostwald ripening at ultra-high volume fractions of the coarsening phase. Modelling and Simulation in Materials Science and Engineering, 23 (6), 065001.
   Web of Science ®Google Scholar
• Tobler, W.R., 1970. A computer movie simulating urban growth in the detroit region. Economic Geography, 46, 234–240.
   Web of Science ®Google Scholar
• Vega-Oliveros, D.A., et al., 2019. From spatio-temporal data to chronological networks. 675–682.
   Google Scholar
• Wang, D., et al., 2020b. A CUDA-based parallel geographically weighted regression for large-scale geographic data. ISPRS International Journal of Geo-Information, 9 (11), 653. doi:.
   Web of Science ®Google Scholar
• Wang, D., Miwa, T., and Morikawa, T., 2020a. Big trajectory data mining: a survey of methods, applications, and services. Sensors, 20 (16), 4571.
   Web of Science ®Google Scholar
• Wang, F., et al., 2017. Local indicator of colocation quotient with a statistical significance test: examining spatial association of crime and facilities. The Professional Geographer, 69 (1), 22–31.
   Web of Science ®Google Scholar
• Wang, Z., and Lam, N.S.N., 2020. Extending getis–ord statistics to account for local space–time autocorrelation in spatial panel data. The Professional Geographer, 72 (3), 411–420.
   Web of Science ®Google Scholar
• Weisburd, D., 2015. The law of crime concentration and the criminology of place. Criminology, 53 (2), 133–157.
   Web of Science ®Google Scholar
• Wu, C., et al. 2018. Multiscale geographically and temporally weighted regression: exploring the spatiotemporal determinants of housing prices. International Journal of Geographical Information Science, 33 (3), 489-511.
   Web of Science ®Google Scholar
• Xu, Q.-S., and Liang, Y. Z., 2001. Monte Carlo cross validation. Chemometrics and Intelligent Laboratory Systems, 56 (1), 1–11.
   Web of Science ®Google Scholar
• Yang, B., et al., 2020. A spatio-temporal method for crime prediction using historical crime data and transitional zones identified from nightlight imagery. International Journal of Geographical Information Science, 34 (9), 1740–1725.
   Web of Science ®Google Scholar
• Yousfi, S., et al., 2020. Mass appraisal without statistical estimation: a simplified comparable sales approach based on a spatiotemporal matrix. The Annals of Regional Science, 64 (2), 349–365.
   Web of Science ®Google Scholar
• Yuan, K., et al., 2019. A quad-tree-based fast and adaptive Kernel Density Estimation algorithm for heat-map generation. International Journal of Geographical Information Science, 33 (12), 2455–2476.
   Web of Science ®Google Scholar
• Yue, H., et al., 2017. The local colocation patterns of crime and land-use features in Wuhan. ISPRS International Journal of Geo-Information, 6 (10), 307.
   Web of Science ®Google Scholar
• Zacharov, P., Rezacova, D., and Brozkova, R., 2013. Evaluation of the QPF of convective flash flood rainfalls over the Czech territory in 2009. Atmospheric Research, 131, 95–107.
   Web of Science ®Google Scholar
• Zahnow, R., and Corcoran, J., 2021. Crime and bus stops: An examination using transit smart card and crime data. Environment and Planning B Urban Analytics and City Science, 48 (4), 706–723.
   PubMedGoogle Scholar
• Zhang, G., Zhu, A.X., and Huang, Q., 2017. A GPU-accelerated adaptive kernel density estimation approach for efficient point pattern analysis on spatial big data. International Journal of Geographical Information Science, 31 (10), 2068–2097.
   Web of Science ®Google Scholar
• Zhang, L., Cheng, J., and Jin, C., 2019. Spatial interaction modeling of OD flow data: comparing geographically weighted negative binomial regression (GWNBR) and OLS (GWOLSR). ISPRS International Journal of Geo-Information, 8 (5), 220.
   Web of Science ®Google Scholar