References
- Anselin, L., I. Syabri, and Y. Kho. 2009. “GeoDa: An Introduction to Spatial Data Analysis.” In Handbook of Spatial Data Analysis, edited by M. Fischer and A. Getis, pp. 73–89. Berlin, Heidelberg and New York: Springer.
- Ashby, M. P. J. 2019. “Studying Crime and Place with the Crime Open Database.” Research Data Journal for the Humanities and Social Sciences 4 (1): 65–80. doi:https://doi.org/10.1163/24523666-00401007.
- Bailey, T., and A. Gatrell. 1995. Interactive Spatial Data Analysis. Essex, England: Longman Scientific and Technical.
- Beairsto, J., Y. Tian, L. Zheng, Q. Zhao, and J. Hong. 2021. “Identifying Locations for New Bike-Sharing Stations in Glasgow: An Analysis of Spatial Equity and Demand Factors.” Annals of GIS 0 (0): 1–16. doi:https://doi.org/10.1080/19475683.2021.1936172.
- Bogomolov, A., B. Lepri, J. Staiano, N. Oliver, F. Pianesi, and A. Pentland. 2014. “Once upon a Crime: Towards Crime Prediction from Demographics and Mobile Data.” Proceedings of the 16th ACM international conference on multimodal interaction (ICMI) 12–16 November 2014. Istanbul Turkey: 427–434. doi: https://doi.org/10.1145/2663204.2663254. https://dl.acm.org/doi/proceedings/10.1145/2663204
- Cahill, M., and G. Mulligan. 2007. “Using Geographically Weighted Regression to Explore Local Crime Patterns.” Social Science Computer Review 25 (2): 174–193. doi:https://doi.org/10.1177/0894439307298925.
- Caplan, J., L. Kennedy, and J. Miller. 2011. “Risk Terrain Modeling: Brokering Criminological Theory and GIS Methods for Crime Forecasting.” Justice Quarterly 28: 360–381. doi:https://doi.org/10.1080/07418825.2010.486037.
- Castells, M. 1998. End of Millennium, the Information Age: Economy, Society and Culture Vol. III. Cambridge, MA; Oxford, UK: Blackwell.
- Chainey, S. 2020. Understanding Crime: Analyzing the Geography of Crime. Redlands, California: ESRI Press.
- Chen, X., Y. Cho, and S. Y. Jang. 2015. “Crime Prediction Using Twitter Sentiment and Weather.” 2015 Systems and Information Engineering Design Symposium 24 April 2015. Charlottesville, Virginia, USA: 63–68, doi: https://doi.org/10.1109/SIEDS.2015.7117012.
- Foth, M., J. H. Choi, and C. Satchell. 2011. Urban Informatics. Conference on Computer Supported Cooperative Work (CSCW). New York, NY, United States: Association for Computing Machinery, 1–8. https://doi.org/10.1145/1958824.1958826.
- Fotheringham, A. S., C. Brunsdon, and M. Charlton. 2002. Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley.
- Haining, R. P., and G. Li. 2020. Modelling Spatial and Spatial-temporal Data: A Bayesian Approach, 608. Boca Raton: CRC Press.
- Hart, T. C., K. M. Lersch, and M. Chataway. 2020. Space, Time, and Crime. Durham, North Carolina: Carolina Academic Press.
- Hladík, J., D. Snopková, M. Lichter, L. Herman, and M. Konečný. 2021. “Spatial-Temporal Analysis of Retail and Services Using Facebook Places Data: A Case Study in Brno, Czech Republic.” Annals of GIS 0 (0): 1–19. doi:https://doi.org/10.1080/19475683.2021.1921846.
- Hu, T., X. Zhu, L. Duan, W. Guo, and E. Arcaute. 2018. “Urban Crime Prediction Based on Spatio-temporal Bayesian Model.” PLoS ONE 13 (10): e0206215. doi:https://doi.org/10.1371/journal.pone.0206215.
- Hutt, O. K., K. Bowers, and S. D. Johnson. 2021. “The Effect of GPS Refresh Rate on Measuring Police Patrol in Micro-places.” Crime Science 10: 3. doi:https://doi.org/10.1186/s40163-021-00140-1.
- Janowicz, K., S. Gao, G. McKenzie, Y. Hu, and B. Bhaduri. 2020. “GeoAI: Spatially Explicit Artificial Intelligence Techniques for Geographic Knowledge Discovery and Beyond.” International Journal of Geographical Information Science 34 (4): 625–636. doi:https://doi.org/10.1080/13658816.2019.1684500.
- Kandt, J., and M. Batty. 2021. “Smart Cities, Big Data and Urban Policy: Towards Urban Analytics for the Long Run.” Cities 109: 102992. doi:https://doi.org/10.1016/j.cities.2020.102992.
- Kikuchi, G., M. Amemiya, and T. Shimada. 2012. “An Analysis of Crime Hot Spots Using GPS Tracking Data of Children and Agent-based Simulation Modeling.” Annals of GIS 18 (3): 207–223. doi:https://doi.org/10.1080/19475683.2012.691902.
- Kim, Y., Y.-J. Byon, and H. Yeo. 2018. “Correction: Enhancing Healthcare Accessibility Measurements Using GIS: A Case Study in Seoul, Korea.” PLOS ONE 13 (3): e0194849. doi:https://doi.org/10.1371/journal.pone.0194849.
- Law, J., M. Quick, and A. Jadavji. 2020. “A Bayesian Spatial Shared Component Model for Identifying Crime-general and Crime-specific Hotspots.” Annals of GIS 26 (1): 65–79. doi:https://doi.org/10.1080/19475683.2020.1720290.
- Law, S., C. I. Seresinhe, Y. Shen, and M. Gutierrez-Roig. 2020. “Street-Frontage-Net: Urban Image Classification Using Deep Convolutional Neural Networks.” International Journal of Geographical Information Science 34 (4): 681–707. doi:https://doi.org/10.1080/13658816.2018.1555832.
- Li, Y. 2021. “Health Resilience among European Countries in the Face of Pandemic: Reflections on European Countries’ Preparedness for COVID-19. In Mapping COVID-19 in Space and Time: Understanding the Spatial and Temporal Dynamics of a Global Pandemic.” In Human Dynamics in Smart Cities Book Series (HDSC), edited by Shaw,Shih-Lung and Sui, Daniel, 309. Switzerland: Springer. doi:https://doi.org/10.1007/978-3-030-72808-3_16.
- Lindegaard, M. R., and W. Bernasco. 2018. “Lessons Learned from Crime Caught on Camera.” Journal of Research in Crime and Delinquency 55 (1): 155–186. doi:https://doi.org/10.1177/0022427817727830.
- Liu, L., H. Zhou, M. Lan, and Z. Wang. 2020. “Linking Luojia 1-01 Nightlight Imagery to Urban Crime.” Applied Geography 125: 102267. doi:https://doi.org/10.1016/j.apgeog.2020.102267.
- Maldonado-Guzmán, D. J. 2020. “Airbnb and Crime in Barcelona (Spain): Testing the Relationship Using a Geographically Weighted Regression.” Annals of GIS 0 (0): 1–14. doi:https://doi.org/10.1080/19475683.2020.1831603.
- Mansour, S., T. Al-Awadhi, N. Al Nasiri, and A. Al Balushi. 2020. “Modernization and Female Labour Force Participation in Oman: Spatial Modelling of Local Variations.” Annals of GIS 0 (0): 1–15. doi:https://doi.org/10.1080/19475683.2020.1768437.
- Ros’es, R., C. Kadar, and N. Malleson. 2021. “A Data-driven Agent-based Simulation to Predict Crime Patterns in an Urban Environment.” Computers, Environment and Urban Systems 89: 101660. doi:https://doi.org/10.1016/j.compenvurbsys.2021.101660.
- Sarim, M., Q. Zhao, and N. Bailey. 2021. “Citizen Mobility and the Growth of Infections During the COVID-19 Pandemic with the Effects of Government Restrictions in Western Europe.” In Mapping COVID-19 in Space and Time: Understanding the Spatial and Temporal Dynamics of a Global Pandemic, edited by S.-L. Shaw and D. Sui, pp. 279–294. Springer International Publishing. doi:https://doi.org/10.1007/978-3-030-72808-3_14.
- Shi, W., M. Goodchild, M. Batty, M. Kwan, and A. Zhang. 2021. Urban Informatics. Singapore: Springer. doi:https://doi.org/10.1007/978-981-15-8983-6.
- Sinclair, M., Q. Zhao, N. Bailey, S. Maadi, and J. Hong. 2021. “Understanding the Use of Greenspace before and during the COVID-19 Pandemic by Using Mobile Phone App Data.” GIScience 2021. https://doi.org/10.25436/E2D59P. September 1.
- Song, G., W. Bernasco, L. Liu, L. Xiao, S. Zhou, and W. Liao. 2019. “Crime Feeds on Legal Activities: Daily Mobility Flows Help to Explain Thieves’ Target Location Choices.” Journal of Quantitative Criminology 35: 831–854. doi:https://doi.org/10.1007/s10940-019-09406-z.
- Tom-Jack, Q., J. Bernstein, and L. Loyola. 2019. “The Role of Geoprocessing in Mapping Crime Using Hot Streets.” ISPRS International Journal of Geo-Information 8: 540. doi:https://doi.org/10.3390/ijgi8120540.
- Tu, W., T. Zhu, C. Zhong, X. Zhang, Y. Xu, and Q. Li. 2021. “Exploring Metro Vibrancy and Its Relationship with Built Environment: A Cross-city Comparison Using Multi-source Urban Data.” Geo-spatial Information Science 1–15. doi:https://doi.org/10.1080/10095020.2021.1996212.
- United Nations Department of Economic and Social Affairs. 2018. “2018 Revision of World Urbanization Prospects.”
- Wang, X., M. S. Gerber, and D. E. Brown. 2012. “Automatic Crime Prediction Using Events Extracted from Twitter Posts.” Social Computing, Behavioral - Cultural Modeling and Prediction, edited by S. J. Yang, A. M. Greenberg and M. Endsley. SBP 2012. Lecture Notes in Computer Science, Vol. 7227. 231–238. Berlin, Heidelberg: Springer.doi: https://doi.org/10.1007/978-3-642-29047-3_28.
- Wang, Z., and Y. Li. 2021. “Could Social Medias Reflect Acquisitive Crime Patterns in London?” Journal of Safety Science and Resilience. doi:https://doi.org/10.1016/j.jnlssr.2021.08.007.
- Wang, C., and F. Wang. 2022. “GIS-Automated Delineation of Hospital Service Areas in Florida: From Dartmouth Method to Network Community Detection Methods.” Annals of GIS 0 (0): 1–17. doi:https://doi.org/10.1080/19475683.2022.2026470.
- Woodworth, J. T., G. O. Mohler, A. L. Bertozzi, and P. J. Brantingham. 2014. “Non-local Crime Density Estimation Incorporating Housing Information.” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 372: 2028. doi:https://doi.org/10.1098/rsta.2013.0403.
- Wu, M., and Q. Huang. 2022. “Human Movement Patterns of Different Racial-Ethnic and Economic Groups in U.S. Top 50 Populated Cities: What Can Social Media Tell Us About Isolation?.” Annals of GIS 0 (0): 1–23. doi:https://doi.org/10.1080/19475683.2022.2026471.
- Xu, J., and Y. Qiang. 2021. “Analysing Information Diffusion in Natural Hazards Using Retweets—a Case Study of 2018 Winter Storm Diego.” Annals of GIS 0 (0): 1–15. doi:https://doi.org/10.1080/19475683.2021.1954086.
- Yang, Z., Y. Chen, Z. Zheng, and Z. Wu. 2022. “Identifying China’s Polycentric Cities and Evaluating the Urban Centre Development Level Using Luojia-1A Night-Time Light Data.” Annals of GIS 0 (0): 1–11. doi:https://doi.org/10.1080/19475683.2022.2026472.
- Zuo, C., L. Ding, Z. Yang, and L. Meng. 2022. “Multiscale Geovisual Analysis of Knowledge Innovation Patterns Using Big Scholarly Data.” Annals of GIS 0 (0): 1–16. doi:https://doi.org/10.1080/19475683.2022.2027012.