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@article{bb328801,
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@article{bb328802,
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Digital Sketch Maps: A Hungarian Multicity Study",
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@article{bb328803,
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@article{bb328807,
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@article{bb328808,
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@article{bb328809,
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@article{bb328810,
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@article{bb328819,
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@article{bb328820,
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@article{bb328854,
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Residential Neighborhood Property Crimes",
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@article{bb328855,
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Influence Urban Crime Patterns",
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Pattern of Theft from the Person: The Perspective from Activity Space",
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@article{bb328857,
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        TITLE = "Modelling & Analysis of High Impact Terrorist Attacks in India
& Its Neighbors",
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@article{bb328858,
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the Pre-COVID-19 and COVID-19 Periods: A Case Study of Haining City,
Zhejiang, China",
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@article{bb328859,
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Spatial Perspective",
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@article{bb328860,
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@article{bb328861,
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Objects Based on Time Geography:
A Case Study of Crime Events in the City of London",
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@article{bb328862,
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Application to Real-Time Systems for Crime Deterrence, Prevention and
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Heterogeneity of Crime Distribution",
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@article{bb328864,
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        TITLE = "A High-Resolution Spatial Distribution-Based Integration Machine
Learning Algorithm for Urban Fire Risk Assessment: A Case Study in
Chengdu, China",
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@article{bb328865,
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Highly Frequented Protected Area",
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        NUMBER = "11",
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@article{bb328866,
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from the 'Franeleros' Experience",
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@article{bb328867,
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Patterns in London during the Pandemic: An Empirical Analysis Using
Geo-Tagged Big Data",
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@article{bb328868,
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the Distribution of Urban Greenspace: The Case of Porto, Portugal",
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Regression Modelling Impacts of COVID-19 Lockdowns on the
Spatiotemporal Patterns of London's Crimes",
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        NUMBER = "1",
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Residents' Mobile Phone Data",
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        TITLE = "Semantics and Geography Aware Hierarchical Learning for Sequential
Crime Prediction",
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@article{bb328872,
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@article{bb328873,
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@article{bb328876,
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Mexico City Before, During, and After the COVID-19 Pandemic",
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p-Median Location Models",
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