The Effectiveness of Big Data-Driven Predictive Policing: Systematic Review

Figures & data

Table 1. Conceptualizing predictive policing.

Component	Content	In
Source	“big data”	Sandhu and Fussey (2021); Benbouzid (2019)
	“data from different sources”	Pearsall (2010)
	“power of information”	NIJ (2014)
	“a broad variety of sorts of data”	Meijer and Wessels (2019)
	“data of one or more type are ingested”	Brantingham (2017)
Analysis	“analytical techniques particularly quantitative techniques”	Perry (2013)
	“algorithmic technologies”	Sandhu and Fussey (2021)
	“advanced analytics”	Beck and McCue (2009)
	“mathematical algorithms”	Egbert and Krasmann (2020)
	“algorithmic methods”	Brantingham (2017)
Purpose	“identify likely targets for police intervention and prevent crime or solve past crimes”	Perry (2013)
	“generate near-future predictions about the people and places deemed likely to be involved in or experience crime”	Sandhu and Fussey (2021)
	“crime reduction; more efficient police agencies; and modern, innovative policing”	Beck and MacCue (2009)
	“anticipate, prevent and respond more effectively to future crime”	Pearsall (2010)
	“to reduce crime and improve public safety”	NIJ (2014)
	“that law enforcers act before criminal activities take place to prevent crime from happening”	Meijer and Wessels (2019)
	“inform strategic and tactical decisions in the field”	Brantingham (2017)

Table 2. Categorized effectiveness verification.

	Tested in the real-world application	Tested displacement effect	Constructed direct prediction model
Type 1	O	O
Type 2		X
Type 3	X		O
Type 4			X

Table 3. Inclusion and exclusion standard.

Criteria		Inclusion	Exclusion
PICO	Population	Policing strategy using big data (big data: multiple data sources including crime data)	Not related to the work of police Only relying on the conventional crime data of police
	Intervention	Predictive policing	Non-predictive policing
	Context	Place-based policing	Person-based policing
	Outcome	Crime reduction Rate of crime prediction (possibly) Efficient crime prevention(quantifiable)	Unquantifiable literature review in terms of efficiency
Technical		Quantitative studies in : academic journal articles Master and doctoral dissertations Books	Qualitative studies Non-academic writings in media (i.e., newspapers or journals)
		Grey Literatures : Policy reports are written by academics or researchers or law enforcement professionals. Governmental policy reports Conference papers and proceedings	Opinion pieces from the individual sphere (i.e., blogs or social media) Policy reports or opinions not written by academics or researchers or law enforcement professionals.
		Language of study: English	Languages other than English
		Studies within 15 years (17 May 2007 − 17 May 2022)	Studies before 2007
		Obtainable	Unobtainable in full-text and abstract

Table 4. Grey literature search.

Sources	Address	Methods
College of Policing	https://www.college. police.uk/	In the search bar, below search terms were applied respectively. “big data” “predict” “predictive”
Global Policing Database	https://gpd.uq.edu.au/s/gpd/ page/about	In the Browse bar(search full-text option), the below search terms were applied respectively. “big data” “predict” “Predictive”
the National Criminal Justice Reference Service	https://www.ojp.gov/ncjrs/ virtual-library/search	In General Search Bar, the below search terms were applied respectively. “big data” “predictive policing”

Figure 1. Keyword-based search.

Figure 2. Citation chaining-backward and forward search.

Figure 3. Number of studies searched and selected.

Table 5. Type 1 and 2 studies.

Type	Authors (year)	Area	Data used for prediction	Target Crime	Police intervention	Result	Additional information
1	Braga and Bond (2008)	US	Emergency call, various qualitative data (local place characteristics, officers’ opinion etc.)	Various types of crimes	Situational interventions, Aggressive interventions (arrest, patrol, etc.), etc.	19.8 % reduction (in general, statistically significant than the control group)	Displacement was tested but no significant effect was detected
1	Carter et al. (2021)	US	Police crime data, drug overdoes data (ER data), crime cost estimation data	Social harm index	Vehicle patrol or foot patrol	The effect of the intervention was significant (p=.0295, social harm cost decreased $38.6 per 10.4 min of policing)	Displacement was tested but no significant effect was detected
1	Ratcliffe et al. (2021)	US	Crime data, demographic data, weather data, etc.	Property and violent crimes	Officer awareness, marked car patrol, unmarked car patrol.	31% reduction only on property crime when applying marked car patrol	No displacement was detected (diffusion of benefit was detected)
2	Wyatt and Alexander (2010)	US	Crime data (number of crimes), traffic accident data, driving under the influence data.	Traffic crashes	Vehicle stops (with visibility like blue lights)	Decreased number of fatal(15.9%) and injury(30.8%) accidents etc after the intervention.	There was no control group compared in the paper.
2	Florence et al. (2011)	UK	Crime data, demographic data, health service records	Violent crimes	Targeted deployment of police resources (presence, CCTV)	Effective in hospital admissions from violence(7->5 per 100k people in treatment while 5->8 in control), and recorded wounding(54 > 82 per 100k people in treatment while 54->114 in control)	There was a significant increase in less serious assaults than the control group
2	Hunt et al. (2014)	US	Crime data, disorder calls, seasonal variations, juvenile arrests	Property crime	Directed patrol etc.	No significant effect on reducing crimes than the control group	The research found that intervention was not properly made in the field.

Figure 4. Type 3 and 4 studies.

Table

No	Author, Year	Target Crime	Region	Data Used (except crime data)
1	Ait El Bour et al., 2018	general crime	US	Local data(site information)
2	Al Boni, 2017	various crime types	US	Social media data
3	Al Boni & Gerber, 2017	various crime types	US	tweet data + etc.
4	Almanie et al., 2015	various types of crime(drug, disorder, theft and etc))	US	Demographics
5	Alves et al., 2018	homicide	Brazil	census data
6	Amiri, 2014	burglary	US	qualitative data
7	Amiruzzaman et al., 2021	crime rate	US	street view
8	Andresen, 2015	various crime types	CA	census data
9	Andresen & Hodgkinson, 2018	property crimes	CA	land use data + etc.
10	Araujo et al., 2019	crime rate	Brazil	urban features
11	Ariel et al., 2015	general crime call	UK	emergency call data (ambulance)
12	Baloian et al., 2017	burglary	Chile	facilities data
13	Bappee et al., 2021	crime rate	CA	streetlight data + etc.
14	Bappee et al., 2021	crime rate	CA	demographics + etc.
15	Belesiotis et al., 2018	various types	UK	street businesses + etc.
16	Bogomolov et al., 2014	crime rate	UK	mobile activity data + etc.
17	Bogomolov et al., 2015	crime rate	UK	weather + etc.
18	Borowik et al., 2018	various crime types	POL	open source
19	Bowen et al., 2018	violence	US	neighborhood census data + etc.
20	Brindha & Thillaikarasi, 2021	assault	US	census(socio-econ data)
21	Briz-Redon et al., 2022	Property / robbery / vandalism	Spain	socio-demographic data + etc.
22	Caplan et al., 2015	burglary	US	spatial factors
23	Caplan et al., 2020	Robbery	US	environmental features
24	Castro et al., 2020	crime rate	Brazil	crime data 2(unoffical) + etc.
25	Chase et al., 2019	none (police deployment and response)	Singapore	Demographics + etc.
26	Chen, 2019	burglary	US	land use data + etc.
27	Chen et al., 2015	Theft	US	Weather + etc.
28	Chen et al., 2016	traffic accident	JAP	mobility data
29	Cichosz, 2020	various types of crime	UK	street features
30	Connealy, 2019	violent crimes(assault robbery, homicide, rape)	US	environment data
31	Connealy, 2021	Street robbery	US	business data + etc.
32	Connealy & Piza, 2019	Robbery	US	business data
33	Corso, 2015	crime rate	US	tweet + etc.
34	Da Silva Neto et al., 2021	next movement of stolen vehicle	Brazil	traffic data
35	Daley et al., 2016	child abuse	US	local features + etc.
36	Dash et al., 2018	various crime types	US	library data + etc.
37	Delts, 2020	various crime types	US	image data + etc.
38	Deshmukh et al., 2020	robbery, theft, murder	India	crime data
39	Do Rego et al., 2020	violent crime	Brazil	regional features
40	Drawve et al., 2016	gun crimes	US	business data
41	Duan et al., 2017	crime rate		311 data(civil complaint)
42	Dugato et al., 2018	bulgrary	Italy	neighbourhood data
43	Ellison et al., 2021	policing demand	UK	census(socio-demographic) + etc.
44	Elluri et al., 2019	crime rate	US	weather + etc.
45	Fatehkia et al., 2019	burglary, assault, robbery	US	Facebook data + etc.
46	Fitterer et al., 2015	Break and entries	CA	urban environment data + etc.
47	Flores et al., 2015	crime risk(homicide)	not given	geodata of public services
48	Garnier et al., 2018	Robbery	US	land use data
49	Gezer, 2017	crime rate	US	education data + etc.
50	Gimenez et al., 2018	car crime accidents	Spain	environment data
51	Gerell, 2018	violent crimes(assault burglary)	Sweden	public environment data + etc.
52	Hajela et al., 2021	various types	US	census data + etc.
53	Han et al., 2020	theft	US	weather
54	He et al., 2020	larceny-theft	China	mobile phone data + etc.
55	Hibdon & Groff, 2014	illegal drug use	US	emergency medical call data
56	Hou et al., 2022	theft and assault	US
57	Hu et al., 2018	burglaries	China	demographics + etc.
58	Huang et al., 2015	various crime types	US	social network data + etc.
59	Huang et al., 2018	various types of crime	US	311 data(civil complaint) + etc.
60	Huang et al., 2019	traffic accident	US	urban complaint data + etc.
61	Hwang et al., 2017	burglary	KOR	social data + etc.
62	Iqbal et al., 2013	crime type	US	census data + etc.
63	Jendryke & McClure, 2021	hate crimes	US	demographic data + etc.
64	Johnson et al., 2021	property crimes	CA	ambient population data
65	Kadar & Pletikosa, 2018	serious crimes(robbery, burglary, assault and etc)	US	census data + etc.
66	Kadar et al., 2016	various crime types	US	demographic data + etc.
67	Kadar et al., 2015	burglary	Swiss	foursquare(local data)
68	Kang & Kang, 2017	crime rate	US	demographic data + etc.
69	Kennedy et al., 2011	gun violence	US	business data
70	Kennedy et al., 2016	assault	US	public housing data
71	Khairuddin et al., 2019	crime rate	US	census data + etc.
72	Kim et al., 2018	crime rate	CA	neighbourhood data
73	Kostakos et al., 2019	crime rate	UK	hotel review data
74	Kounadi et al., 2018	Robbery	Austria	twitter data + etc.
75	Kwon et al., 2021	Theft	KOR	environmental data(land use and etc)
76	Lamari et al., 2020	various crime types	US	socio-economic data + etc.
77	Li et al., 2019	property cirme and violent crime	US	socio-ecnomic + etc.
78	Li et al., 2021	post impact(clean up time and etc)	US	trafic data
79	Liang et al., 2022	Burglary, Robbery, Felony Assault, Grand Larceny	China	Demographics + etc.
80	Liang et al., 2022	various types of crime	China	urban features + etc.
81	Liesenfeld et al., 2017	various types of crime	US	socio-economic data
82	Liu, 2017	violent crime	US	weather + etc.
83	Liu et al., 2021	traffic accident	US	traffic flow data
84	Marchant et al., 2018	violent crimes(domestic violence, burglary, vehicle theft)	AU	demographics
85	Matijosaitiene et al., 2018	Larceny / Harassment / Assault	US	land use geographic data
86	McClendon & Meghanathan et al., 2015	various crime types	US	census data
87	Meng et al., 2018	car crash accident	China	weather data
88	Moosavi et al., 2019	traffic accident	US	weather + etc.
89	Nguyen et al., 2017	various types(street, other, motor vehicle theft, burglary)	US	demographic data
90	Ohyama & Amemiya, 2018	Theft from Vehicle	JAP	facility data
91	Parent et al., 2020	crime rate	CA	socio-economic + etc.
92	Park et al., 2016	traffic accident	KOR	accident data
93	Peng & Kurland, 2014	burglaries	China	demographics + etc.
94	Piña-García & Ramírez-Ramírez, 2019	various types of crime	Mexico	tweet data + etc.
95	Piza & Carter, 2018	Burglary & Motor Vehicle Theft	US	geospatial feature data + etc.
96	Qian et al., 2020	crime rate	US	311 data(civil complaint) + etc.
97	Rahakrishnan & Dervarasan, 2016	crime rate	US	census data
98	Rastogi et al., 2020	crime against women	India	census data
99	Reed, 2015	identity theft	US	demographic data
100	Reinhart, 2016	various crime types	US	socio-economic + etc.
101	Ristea et al., 2020	property crimes	US	tweets + etc.
102	Roses, 2020	various crime types	US	street segment + etc.
103	Roses et al., 2021	crime rate	US	census + etc.
104	Ruiz & Sawant, 2019	various crime tpyes	US	business data + etc.
105	Rumi & Salim, 2020	crime rate	US	check-in data
106	Rumi et al., 2018	theft	US & AU	foursquare(local data) + etc.
107	Rummens & Hardyns, 2020	burglary	Belgium	census data + etc.
108	Rummens & Hardyns, 2021	burglary	Belgium	demographics + etc.
109	Rummens et al., 2017	burglary, robbery, battery	Netherlands	demographics + etc.
110	Rummens et al., 2021	various types of crime	Belgium	mobile data(to find ambient population)
111	Salama et al., 2021	various types of crime	US	weather + etc.
112	Schaffter, 2020	violent crime	US	census data
113	Shukla et al., 2021	crime rate	US	econ variables (like employees) + etc.
114	Solomon et al., 2022	burglary	US. Israel	weather + etc.
115	Sujatha & Ezhilmaran, 2014	general crime rate	US	LEA data + etc.
116	Sujatha & Ezhilmaran, 2016	crime rate	US	census data + etc.
117	Sun et al., 2021	crime rate	US	census data
118	Tang et al., 2019	crime rate	China	building boundary + etc.
119	Tarlekar et al., 2021	crime rate	India	media data + etc.
120	Tavares & Costa, 2021	property crimè	Portugal	demographics + etc.
121	Tian & Zhang, 2021	traffic accident	China	traffic flow data
122	Vinnia & Felix, 2019	crime rate	US	zoning data
123	Vomfell et al., 2018	violent / property	US	taxi flow data + etc.
124	Wang & Brown, 2012	breaking and entering	US	geograhic(highways, businesses, schools and etc) + etc.
125	Wang et al., 2019	property / violent	CA	census data
126	Wang et al., 2017	crime rate	US	Weather
127	Wang et al., 2017	various crime types	US	venue data
128	Wang et al., 2020	various types of crime	US	census data + etc.
129	Wang et al., 2020	crime risk(homicide occurance)	China	demographics + etc.
130	Wang et al., 2013	burglary / robbery / larceny	US	demographics + etc.
131	Wang et al., 2012	personal and property crimes	US	census data
132	Wawrzyniak et al., 2018	various crime types	Poland	meteological data + etc.
133	Wei et al., 2020	various types of crime	US	demographic data + etc.
134	Wheeler & Steenbeek, 2021	Robbery	US	census data
135	Wu et al., 2017	crime rate	US	street view + etc.
136	Xia et al., 2021	drug crime	US	socio-ecnomic data + etc.
137	Yang et al., 2018	varuiys types	US	twitter data + etc.
138	Yang et al., 2020	street crimes	US	night light image based urban transitional zones
139	Yao et al., 2020	crime rate	US	demographic data + etc.
140	Ye et al., 2021	theft	US	311 call data
141	Yi et al., 2018	crime rate	US	census data + etc.
142	Yoo & Wheeler, 2019	homeless related crime	US	environmental features
143	Yuan et al., 2018	traffic accident	US	high-resolution rainfall data + etc.
144	Zhang et al., 2019	theft and assault	US	tweet data
145	Zhang et al., 2020a	various types of crime(person offense, property, theft and etc)	US	GSV(google street view) + etc.
146	Zhang et al., 2020b	theft and assault	US	twitter data
147	Zhang et al., 2021	property crime	China	weather data
148	Zhang et al., 2022	theft	China	skynet camera data + etc.
149	Zhao & Tang, 2017	crime rate	US	meteorological data + etc.
150	Zheng et al., 2023	various crime types	US	tweeter data
151	Zhou et al., 2020	crime rate	US	demographic data + etc.
152	Zhou et al., 2021	crime risk	US	weather + etc.
153	Zhou et al., 2022	road crime	US	road network + etc.
154	Zhu, 2018	burglary	US	social data
155	Zuniga-Garcia et al., 2022	Pedestrian crash	US	road inventory + etc.

Sandhu, A., & Fussey, P. (2021). The ‘uberization of policing’? How police negotiate and operationalise predictive policing technology. Policing and Society, 31(1), 66–81. https://doi.org/10.1080/10439463.2020.1803315

 Web of Science ®Google Scholar

Benbouzid, B. (2019). To predict and to manage. Predictive policing in the United States. Big Data & Society, 6(1), 2053951719861703

 Google Scholar

Pearsall, B. (2010). Predictive policing: The future of law enforcement? National Institute of Justice Journal, 266(1), 16–19.

 Google Scholar

NIJ. (2014). Overview of predictive policing. Retrieved from https://nij.ojp.gov/topics/articles/overview-predictive-policing.

 Google Scholar

Meijer, A., & Wessels, M. (2019). Predictive policing: Review of benefits and drawbacks. International Journal of Public Administration, 42(12), 1031–1039. https://doi.org/10.1080/01900692.2019.1575664

 Web of Science ®Google Scholar

Brantingham, P. J. (2017). The logic of data bias and its impact on place-based predictive policing. Ohio State Journal of Criminal Law, 15, 473.

 Google Scholar

Perry, W. L. (2013). Predictive policing: The role of crime forecasting in law enforcement operations. Rand Corporation.

 Google Scholar

Beck, C., & McCue, C. (2009). Predictive policing: What can we learn from Wal-Mart and Amazon about fighting crime in a recession? Police Chief, 76(11), 18.

 Google Scholar

Egbert, S., & Krasmann, S. (2020). Predictive policing: Not yet, but soon preemptive? Policing and Society, 30(8), 905–919. https://doi.org/10.1080/10439463.2019.1611821

 Web of Science ®Google Scholar

Braga, A. A., & Bond, B. J. (2008). Policing crime and disorder hot spots: A randomized controlled trial. Criminology, 46(3), 577–607. https://doi.org/10.1111/j.1745-9125.2008.00124.x

 Web of Science ®Google Scholar

Carter, J. G., Mohler, G., Raje, R., Chowdhury, N., & Pandey, S. (2021). The Indianapolis harmspot policing experiment. Journal of Criminal Justice, 74(c), 101814. https://doi.org/10.1016/j.jcrimjus.2021.101814

 Google Scholar

Ratcliffe, J. H., Taylor, R. B., Askey, A. P., Thomas, K., Grasso, J., Bethel, K. J., … Koehnlein, J. (2021). The Philadelphia predictive policing experiment. Journal of Experimental Criminology, 17(1), 15–41. https://doi.org/10.1007/s11292-019-09400-2

 Web of Science ®Google Scholar

Wyatt, J., & Alexander, M. (2010). Integrating crime and traffic crash data in Nashville. Geography & Public Safety, 2(3), 9–11.

 Google Scholar

Florence, C., Shepherd, J. P., Brennan, I., & Simon, T. (2011). Effectiveness of anonymised information sharing and use in health service, police, and local government partnership for preventing violence related injury: Experimental study and time series analysis. British Medical Journal, 342, d3313. https://doi.org/10.1136/bmj.d3313.

 PubMedGoogle Scholar

Hunt, P., Saunders, J., & Hollywood, J. S. (2014). Evaluation of the Shreveport predictive policing experiment. Santa Monica: Rand Corporation.

 Google Scholar

Ait El Bour, H., Ounacer, S., Elghomari, Y., Jihal, H., & Azzouazi, M. (2018). A crime prediction model based on spatial and temporal data. Periodicals of Engineering and Natural Sciences (PEN), 6(2), 360–364. https://doi.org/10.21533/pen.v6i2.524

 Google Scholar

Al Boni, M. (2017). Localized crime prediction methods [Doctoral dissertation]. University of Virginia.

 Google Scholar

Al Boni, M., & Gerber, M. S. (2017). Predicting crime with routine activity patterns inferred from social media. In 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2016) - Conference Proceedings.

 Google Scholar

Almanie, T., Mirza, R., & Lor, E. (2015). Crime prediction based on crime types and using spatial and temporal criminal hotspots. International Journal of Data Mining & Knowledge Management Process, 5(4), 01–19. https://doi.org/10.5121/ijdkp.2015.5401

 Google Scholar

Alves, L. G. A., Ribeiro, H. V., & Rodrigues, F. A. (2018). Crime prediction through urban metrics and statistical learning. Physica A: Statistical Mechanics and Its Applications, 505, 435–443. https://doi.org/10.1016/j.physa.2018.03.084

 Web of Science ®Google Scholar

Amiri, S. (2014). Testing a geospatial predictive policing strategy: Application of ArcGIS 3D analyst tools for forecasting commission of residential burglaries [Doctoral dissertation]. Washington State University.

 Google Scholar

Amiruzzaman, M., Curtis, A., Zhao, Y., Jamonnak, S., & Ye, X. (2021). Classifying crime places by neighborhood visual appearance and police geonarratives: A machine learning approach. Journal of Computational Social Science, 4(2), 813–837. https://doi.org/10.1007/s42001-021-00107-x

 PubMedGoogle Scholar

Andresen, M. A. (2015). Predicting local crime clusters using (multinomial) logistic regression. Cityscape, 17, 249–262.

 Google Scholar

Andresen, M. A., & Hodgkinson, T. (2018). Predicting property crime risk: an application of risk terrain modeling in Vancouver, Canada. European Journal on Criminal Policy and Research, 24(4), 373–392. https://doi.org/10.1007/s10610-018-9386-1

 Web of Science ®Google Scholar

Araujo, A., Cacho, N., Bezerra, L., Vieira, C., & Borges, J. (2019). Towards a Crime Hotspot Detection Framework for Patrol Planning. In Proceedings – 20th International Conference on High Performance Computing and Communications, 16th International Conference on Smart City and 4th International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2018.

 Google Scholar

Ariel, B., Weinborn, C., & Boyle, A. (2015). Can routinely collected ambulance data about assaults contribute to reduction in community violence? Emergency Medicine Journal: EMJ, 32(4), 308–313. https://doi.org/10.1136/emermed-2013-203133

 PubMed Web of Science ®Google Scholar

Baloian, N., Bassaletti, E., Fernández, M., Figueroa, O., Fuentes, P., Manasevich, R., … Vergara, M. (2017). Crime prediction using patterns and context. In Proceedings of the 2017 IEEE 21st International Conference on Computer Supported Cooperative Work in Design (CSCWD 2017).

 Google Scholar

Bappee, F. K., Petry, L. M., Soares, A., & Matwin, S. (2021). Analyzing the impact of foursquare and streetlight data with human demographics on future crime prediction. In Advances in data science and information engineering (pp. 435–449). Springer.

 Google Scholar

Belesiotis, A., Papadakis, G., & Skoutas, D. (2018). Analyzing and predicting spatial crime distribution using crowdsourced and open data. ACM Transactions on Spatial Algorithms and Systems, 3(4), 1–31. https://doi.org/10.1145/3190345

 Google Scholar

Bogomolov, A., Lepri, B., Staiano, J., Oliver, N., Pianesi, F., & Pentland, A. (2014, November). Once upon a crime: Towards crime prediction from demographics and mobile data. In Proceedings of the 16th International Conference on Multimodal Interaction (pp. 427–434).

 Google Scholar

Bogomolov, A., Lepri, B., Staiano, J., Letouzé, E., Oliver, N., Pianesi, F., & Pentland, A. (2015). Moves on the street: Classifying crime hotspots using aggregated anonymized data on people dynamics. Big Data, 3(3), 148–158. https://doi.org/10.1089/big.2014.0054

 PubMed Web of Science ®Google Scholar

Borowik, G., Wawrzyniak, Z. M., & Cichosz, P. (2018). December). Time series analysis for crime forecasting. In 2018 26th International Conference on Systems Engineering (ICSEng) (pp. 1–10). IEEE. https://doi.org/10.1109/ICSENG.2018.8638179

 Google Scholar

Bowen, D. A., Mercer Kollar, L. M., Wu, D. T., Fraser, D. A., Flood, C. E., Moore, J. C., … Sumner, S. A. (2018). Ability of crime, demographic and business data to forecast areas of increased violence. International Journal of Injury Control and Safety Promotion, 25(4), 443–448. https://doi.org/10.1080/17457300.2018.1467461

 PubMed Web of Science ®Google Scholar

Brindha, R., & Thillaikarasi, M. (2021). Crime data forecasting using machine learning and big data. Analytics. Special Issue on Computing Technology and Information Management. Webology, 18. 591–606.

 Google Scholar

Briz-Redón, Á., Mateu, J., & Montes, F. (2022). Modeling the influence of places on crime risk through a non-linear effects model: A comparison with risk terrain modeling. Applied Spatial Analysis and Policy, 15(2), 507–527. https://doi.org/10.1007/s12061-021-09410-6

 Web of Science ®Google Scholar

Caplan, J. M., Kennedy, L. W., Barnum, J. D., & Piza, E. L. (2015). Risk terrain modeling for spatial risk assessment. Cityscape: A Journal of Policy Development and Research, 17(1), 7–16.

 Google Scholar

Caplan, J. M., Kennedy, L. W., Piza, E. L., & Barnum, J. D. (2020). Using Vulnerability and Exposure to Improve Robbery Prediction and Target Area Selection. Applied Spatial Analysis and Policy, 13(1), 113–136. https://doi.org/10.1007/s12061-019-09294-7

 Web of Science ®Google Scholar

Castro, U. R., Rodrigues, M. W., & Brandao, W. C. (2020). Predicting crime by exploiting supervised learning on heterogeneous data. In ICEIS (1) (pp. 524–531).

 Google Scholar

Chase, J. D., Nguyen, D. T., Sun, H., & Lau, H. C. (2019). Improving law enforcement daily deployment through machine learning-informed optimization under uncertainty. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19): Macau, August (pp. 10–16).

 Google Scholar

Chen, Y. (2019). Crime mapping powered by machine learning and web GIS [Doctoral dissertation]. California State University, Northridge.

 Google Scholar

Chen, X., Cho, Y., & Jang, S. Y. (2015, April). Crime prediction using Twitter sentiment and weather. In 2015 Systems and Information Engineering Design Symposium (pp. 63–68). IEEE. https://doi.org/10.1109/SIEDS.2015.7117012

 Google Scholar

Chen, Q. J., Song, X., Yamada, H., & Shibasaki, R. (2016, Feb 12-17). Learning deep representation from big and heterogeneous data for traffic accident inference. Paper presented at the 30th Association-for-the-Advancement-of-Artificial-Intelligence (AAAI) Conference on Artificial Intelligence, Phoenix, AZ. https://doi.org/10.1609/aaai.v30i1.10011

 Google Scholar

Cichosz, P. (2020). Urban crime risk prediction using point of interest data. ISPRS International Journal of Geo-Information, 9(7), 459. https://doi.org/10.3390/ijgi9070459

 Web of Science ®Google Scholar

Connealy, N. T. (2021). Understanding the predictors of street robbery hot spots: A matched pairs analysis and systematic social observation. Crime & Delinquency, 67(9), 1319–1352. https://doi.org/10.1177/0011128720926116

 Web of Science ®Google Scholar

Connealy, N. T., & Piza, E. L. (2019). Risk factor and high-risk place variations across different robbery targets in Denver, Colorado. Journal of Criminal Justice, 60, 47–56. https://doi.org/10.1016/j.jcrimjus.2018.11.003

 Web of Science ®Google Scholar

Corso, A. (2015). A three-part exploration linking social media, big data, and GIS: A case of predictive crime analysis [Doctoral dissertation]. The Claremont Graduate University.

 Google Scholar

Da Silva Neto, J. S., Coelho Da Silva, T. L., Cruz, L. A., Monteiro De Lira, V., De MacEdo, J. A. F., Pires Magalhaes, R., & Peres, L. G. (2021). Predicting the next location for trajectories from stolen vehicles. In Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI.

 Google Scholar

Daley, D., Bachmann, M., Bachmann, B. A., Pedigo, C., Bui, M. T., & Coffman, J. (2016). Risk terrain modeling predicts child maltreatment. Child Abuse & Neglect, 62, 29–38. https://doi.org/10.1016/j.chiabu.2016.09.014

 PubMed Web of Science ®Google Scholar

Dash, S. K., Safro, I., & Srinivasamurthy, R. S. (2018, December). Spatio-temporal prediction of crimes using network analytic approach. In 2018 IEEE International Conference on Big Data (Big Data) (pp. 1912–1917). https://doi.org/10.1109/BigData.2018.8622041

 Google Scholar

Delts, R. G. (2020). A geostatistical analysis of crime in seattle considering infrastructure and data-mined colocation [Doctoral dissertation]. George Mason University.

 Google Scholar

Deshmukh, A., Banka, S., Dcruz, S. B., Shaikh, S., & Tripathy, A. K. (2020). Safety app: Crime prediction using GIS. In 2020 3rd International Conference on Communication Systems, Computing and IT Applications (CSCITA 2020) - Proceedings.

 Google Scholar

Do Rêgo, L. G. C., Da Silva, T. L. C., Magalhães, R. P., De MacÊdo, J. A. F., & Silva, W. C. P. (2020). Exploiting points of interest for predictive policing. In Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities (ARIC 2020). https://doi.org/10.1145/3423455.3430319

 Google Scholar

Drawve, G., Moak, S. C., & Berthelot, E. R. (2016). Predictability of gun crimes: A comparison of hot spot and risk terrain modelling techniques. Policing and Society, 26(3), 312–331. https://doi.org/10.1080/10439463.2014.942851

 Web of Science ®Google Scholar

Duan, L., Hu, T., Cheng, E., Zhu, J., & Gao, C. (2017). Deep convolutional neural networks for spatiotemporal crime prediction. In Proceedings of the International Conference on Information and Knowledge Engineering (IKE) (pp. 61–67).

 Google Scholar

Dugato, M., Favarin, S., & Bosisio, A. (2018). Isolating target and neighbourhood vulnerabilities in crime forecasting. European Journal on Criminal Policy and Research, 24(4), 393–415. https://doi.org/10.1007/s10610-018-9385-2

 Web of Science ®Google Scholar

Ellison, M., Bannister, J., Lee, W. D., & Haleem, M. S. (2021). Understanding policing demand and deployment through the lens of the city and with the application of big data. Urban Studies, 58(15), 3157–3175. https://doi.org/10.1177/0042098020981007

 Web of Science ®Google Scholar

Elluri, L., Mandalapu, V., & Roy, N. (2019, June). Developing machine learning based predictive models for smart policing. In 2019 IEEE International Conference on Smart Computing (SMARTCOMP) (pp. 198–204). https://doi.org/10.1109/SMARTCOMP.2019.00053

 Google Scholar

Fatehkia, M., O'Brien, D., & Weber, I. (2019). Correlated impulses: Using Facebook interests to improve predictions of crime rates in urban areas. PloS One, 14(2), e0211350. https://doi.org/10.1371/journal.pone.0211350

 PubMed Web of Science ®Google Scholar

Fitterer, J., Nelson, T. A., & Nathoo, F. (2015). Predictive crime mapping. Police Practice and Research, 16(2), 121–135. https://doi.org/10.1080/15614263.2014.972618

 Web of Science ®Google Scholar

Flores, P., Vergara, M., Fuentes, P., Jaramillo, F., Acuña, D., Perez, A., & Orchard, M. (2015). Modeling and prediction of criminal activity based on spatio-temporal probabilistic risk functions. In Annual Conference of the PHM Society, 7(1). https://doi.org/10.36001/phmconf.2015.v7i1.2676

 Google Scholar

Garnier, S., Caplan, J. M., & Kennedy, L. W. (2018). Predicting dynamical crime distribution from environmental and social influences. Frontiers in Applied Mathematics and Statistics, 4, 13. https://doi.org/10.3389/fams.2018.00013

 Google Scholar

Gezer, F. (2017). Spatio-temporal modeling of the US college crime data [Doctoral dissertation]. University of Delaware.

 Google Scholar

Giménez-Santana, A., Medina-Sarmiento, J. E., & Miró-Llinares, F. (2018). Risk terrain modeling for road safety: Identifying crash-related environmental factors in the province of Cádiz, Spain. European Journal on Criminal Policy and Research, 24(4), 451–467. https://doi.org/10.1007/s10610-018-9398-x

 Web of Science ®Google Scholar

Gerell, M. (2018). Bus stops and violence, are risky places really risky? European Journal on Criminal Policy and Research, 24(4), 351–371. https://doi.org/10.1007/s10610-018-9382-5

 Web of Science ®Google Scholar

Hajela, G., Chawla, M., & Rasool, A. (2021). Crime hotspot prediction based on dynamic spatial analysis. ETRI Journal, 43(6), 1058–1080. https://doi.org/10.4218/etrij.2020-0220

 Web of Science ®Google Scholar

Han, X., Hu, X., Wu, H., Shen, B., & Wu, J. (2020). Risk prediction of theft crimes in urban communities: An integrated model of LSTM and ST-GCN. IEEE Access, 8, 217222–217230. https://doi.org/10.1109/ACCESS.2020.3041924

 Web of Science ®Google Scholar

He, L., Páez, A., Jiao, J., An, P., Lu, C., Mao, W., & Long, D. (2020). Ambient population and larceny-theft: A spatial analysis using mobile phone data. ISPRS International Journal of Geo-Information, 9(6), 342. https://doi.org/10.3390/ijgi9060342

 Web of Science ®Google Scholar

Hibdon, J., & Groff, E. R. (2014). What you find depends on where you look: Using emergency medical services call data to target illicit drug use hot spots. Journal of Contemporary Criminal Justice, 30(2), 169–185. https://doi.org/10.1177/1043986214525077

 Web of Science ®Google Scholar

Hou, M., Hu, X., Cai, J., Han, X., & Yuan, S. (2022). An integrated graph model for spatial–temporal urban crime prediction based on attention mechanism. ISPRS International Journal of Geo-Information, 11(5), 294. https://doi.org/10.3390/ijgi11050294

 Web of Science ®Google Scholar

Hu, T., Zhu, X., Duan, L., & Guo, W. (2018). Urban crime prediction based on spatiotemporal Bayesian model. PloS One, 13(10), e0206215. https://doi.org/10.1371/journal.pone.0206215

 PubMed Web of Science ®Google Scholar

Huang, Y. Y., Li, C. T., & Jeng, S. K. (2015, October). Mining location-based social networks for criminal activity prediction. In 2015 24th Wireless and Optical Communication Conference (WOCC) (pp. 185–189). IEEE. https://doi.org/10.1109/WOCC.2015.7346202

 Google Scholar

Huang, C., Zhang, J., Zheng, Y., & Chawla, N. V. (2018, October). DeepCrime: Attentive hierarchical recurrent networks for crime prediction. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management (pp. 1423–1432).

 Google Scholar

Huang, C., Zhang, C., Dai, P., & Bo, L. (2019, November). Deep dynamic fusion network for traffic accident forecasting. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (pp. 2673–2681). https://doi.org/10.1145/3357384.3357829

 Google Scholar

Hwang, Y., Jung, S., Lee, J., & Jeong, Y. (2017). Predicting residential burglaries based on building elements and offender behavior: Study of a row house area in Seoul, Korea. Computers, Environment and Urban Systems, 61, 94–107. https://doi.org/10.1016/j.compenvurbsys.2016.09.004

 Web of Science ®Google Scholar

Iqbal, R., Murad, M. A. A., Mustapha, A., Panahy, P. H. S., & Khanahmadliravi, N. (2013). An experimental study of classification algorithms for crime prediction. Indian Journal of Science and Technology, 6(3), 1–7. https://doi.org/10.17485/ijst/2013/v6i3.6

 Google Scholar

Jendryke, M., & McClure, S. C. (2021). Spatial prediction of sparse events using a discrete global grid system; a case study of hate crimes in the USA. International Journal of Digital Earth, 14(6), 789–805. https://doi.org/10.1080/17538947.2021.1886356

 Web of Science ®Google Scholar

Johnson, P., Andresen, M. A., & Malleson, N. (2021). Cell towers and the ambient population: A spatial analysis of disaggregated property crime. European Journal on Criminal Policy and Research, 27(3), 313–333. https://doi.org/10.1007/s10610-020-09446-3

 Web of Science ®Google Scholar

Kadar, C., & Pletikosa, I. (2018). Mining large-scale human mobility data for long-term crime prediction. EPJ Data Science, 7(1), 1–27. https://doi.org/10.1140/epjds/s13688-018-0150-z

 Google Scholar

Kadar, C., Iria, J., & Pletikosa Cvijikj, I. (2016). Exploring Foursquare-derived features for crime prediction in New York City. In The 5th International Workshop on Urban Computing (UrbComp 2016). ACM.

 Google Scholar

Kadar, C., Zanni, G., Vogels, T., & Cvijikj, I. P. (2015). Towards a burglary risk profiler using demographic and spatial factors. In Web Information Systems Engineering–WISE 2015: 16th International Conference, Miami, FL, USA, November 1-3, 2015, Proceedings, Part I (pp. 586–600 Springer International Publishing.

 Google Scholar

Kang, H. W., & Kang, H. B. (2017). Prediction of crime occurrence from multimodal data using deep learning. PloS One, 12(4), e0176244. https://doi.org/10.1371/journal.pone.0176244

 PubMed Web of Science ®Google Scholar

Kennedy, L. W., Caplan, J. M., & Piza, E. (2011). Risk clusters, hotspots, and spatial intelligence: Risk terrain modeling as an algorithm for police resource allocation strategies. Journal of Quantitative Criminology, 27(3), 339–362. https://doi.org/10.1007/s10940-010-9126-2

 Web of Science ®Google Scholar

Kennedy, L. W., Caplan, J. M., Piza, E. L., & Buccine-Schraeder, H. (2016). Vulnerability and exposure to crime: Applying risk terrain modeling to the study of assault in Chicago. Applied Spatial Analysis and Policy, 9(4), 529–548. https://doi.org/10.1007/s12061-015-9165-z

 Web of Science ®Google Scholar

Khairuddin, A. R., Alwee, R., & Harun, H. (2019). Comparative study on artificial intelligence techniques in crime forecasting. Applied Mechanics and Materials, 892, 94–100. https://doi.org/10.4028/www.scientific.net/AMM.892.94

 Google Scholar

Kim, S., Joshi, P., Kalsi, P. S., & Taheri, P. (2018, November). Crime analysis through machine learning. In 2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON) (pp. 415–420). IEEE. https://doi.org/10.1109/IEMCON.2018.8614828

 Google Scholar

Kostakos, P., Robroo, S., Lin, B., & Oussalah, M. (2019). November). Crime prediction using hotel reviews?. In 2019 European Intelligence and Security Informatics Conference (EISIC) (pp. 134–137). IEEE. https://doi.org/10.1109/EISIC49498.2019.9108861

 Google Scholar

Kounadi, O., Ristea, A., Leitner, M., & Langford, C. (2018). Population at risk: Using areal interpolation and Twitter messages to create population models for burglaries and robberies. Cartography and Geographic Information Science, 45(3), 205–220. https://doi.org/10.1080/15230406.2017.1304243

 PubMed Web of Science ®Google Scholar

Kwon, E., Jung, S., & Lee, J. (2021). Artificial neural network model development to predict theft types in consideration of environmental factors. ISPRS International Journal of Geo-Information, 10(2), 99. https://doi.org/10.3390/ijgi10020099

 Web of Science ®Google Scholar

Lamari, Y., Freskura, B., Abdessamad, A., Eichberg, S., & de Bonviller, S. (2020). Predicting spatial crime occurrences through an efficient ensemble-learning model. ISPRS International Journal of Geo-Information, 9(11), 645. https://doi.org/10.3390/ijgi9110645

 Web of Science ®Google Scholar

Li, T., Huang, Y., Evans, J., & Chattopadhyay, I. (2019). Long-range event-level prediction and response simulation for urban crime and global terrorism with Granger networks. https://arxiv.org/abs/1911.05647

 Google Scholar

Li, D., Wu, J., & Peng, D. (2021). Online traffic accident spatial-temporal post-impact prediction model on highways based on spiking neural networks. Journal of Advanced Transportation, 2021, 1–20. https://doi.org/10.1155/2021/9290921

 Web of Science ®Google Scholar

Liang, W., Wu, Z., Li, Z., & Ge, Y. (2022). CrimeTensor: Fine-scale crime prediction via tensor learning with spatiotemporal consistency. ACM Transactions on Intelligent Systems and Technology, 13(2), 1–24. https://doi.org/10.1145/3501807

 Web of Science ®Google Scholar

Liesenfeld, R., Richard, J. F., & Vogler, J. (2017). Likelihood-based inference and prediction in spatio-temporal panel count models for urban crimes. Journal of Applied Econometrics, 32(3), 600–620. https://doi.org/10.1002/jae.2534

 Web of Science ®Google Scholar

Liu, X. (2017). Temporal and spatiotemporal models for short-term crime prediction [Doctoral dissertation]. Illinois Institute of Technology.

 Google Scholar

Liu, W., Liu, X., Feng, H., Wang, Y., Guan, L., Xu, W., … Kong, X. (2021, October). ST-TAP: A traffic accident prediction framework based on spatio-temporal transformer. In 2021 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech) (pp. 360–365). IEEE. https://doi.org/10.1109/DASC-PICom-CBDCom-CyberSciTech52372.2021.00068

 Google Scholar

Marchant, R., Haan, S., Clancey, G., & Cripps, S. (2018). Applying machine learning to criminology: Semi-parametric spatial-demographic Bayesian regression. Security Inform, 7(1), 1–19.

 Google Scholar

Matijosaitiene, I., Zhao, P., Jaume, S., & Gilkey, J. W. Jr. (2018). Prediction of hourly effect of land use on crime. ISPRS International Journal of Geo-Information, 8(1), 16. https://doi.org/10.3390/ijgi8010016

 Web of Science ®Google Scholar

McClendon, L., & Meghanathan, N. (2015). Using machine learning algorithms to analyze crime data. Machine Learning and Applications: An International Journal, 2(1), 1–12. https://doi.org/10.5121/mlaij.2015.2101

 Google Scholar

Meng, H., Wang, X., & Wang, X. (2018, November). Expressway crash prediction based on traffic big data. In Proceedings of the 2018 International Conference on Signal Processing and Machine Learning (pp. 11–16). https://doi.org/10.1145/3297067.3297093

 Google Scholar

Moosavi, S., Samavatian, M. H., Parthasarathy, S., Teodorescu, R., & Ramnath, R. (2019, November). Accident risk prediction based on heterogeneous sparse data: New dataset and insights. In Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (pp. 33–42).

 Google Scholar

Nguyen, T. T., Hatua, A., & Sung, A. H. (2017). Building a learning machine classifier with inadequate data for crime prediction. Journal of Advances in Information Technology, 8(2), 141–147. https://doi.org/10.12720/jait.8.2.141-147

 Google Scholar

Ohyama, T., & Amemiya, M. (2018). Applying crime prediction techniques to Japan: A comparison between risk terrain modeling and other methods. European Journal on Criminal Policy and Research, 24(4), 469–487. https://doi.org/10.1007/s10610-018-9378-1

 Web of Science ®Google Scholar

Parent, M., Roy, A., Gagnon, C., Lemaire, N., Deslauriers-Varin, N., Falk, T. H., & Tremblay, S. (2020). Designing an explainable predictive policing model to forecast police workforce distribution in cities. Canadian Journal of Criminology and Criminal Justice, 62(4), 52–76. https://doi.org/10.3138/cjccj.2020-0011

 Web of Science ®Google Scholar

Park, S. H., Kim, S. M., & Ha, Y. G. (2016). Highway traffic accident prediction using VDS big data analysis. The Journal of Supercomputing, 72(7), 2815–2831. https://doi.org/10.1007/s11227-016-1624-z

 Web of Science ®Google Scholar

Peng, C., & Kurland, J. (2014). The agent-based spatial simulation to burglary in Beijing. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8582 LNCS (pp. 31–43).

 Google Scholar

Piña-García, C. A., & Ramírez-Ramírez, L. (2019). Exploring crime patterns in Mexico City. Journal of Big Data, 6(1), 1–21. https://doi.org/10.1186/s40537-019-0228-x

 PubMedGoogle Scholar

Piza, E. L., & 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), 842–870. https://doi.org/10.1080/07418825.2017.1342854

 Web of Science ®Google Scholar

Qian, Y., Pan, L., Wu, P., & Xia, Z. (2020, July). GeST: A grid embedding based spatio-temporal correlation model for crime prediction. In 2020 IEEE Fifth International Conference on Data Science in Cyberspace (DSC) (pp. 1–7). IEEE. https://doi.org/10.1109/DSC50466.2020.00009

 Google Scholar

Radhakrishnan, S., & Devarasan, E. (2016). Computing the probability on socio economic factors to predict the crime locations by means of joint probability based AMABC-FCIL. International Journal of Intelligent Engineering and Systems, 9(3), 80–90. https://doi.org/10.22266/ijies2016.0930.08

 Google Scholar

Rastogi, A., Sridhar, S., & Gupta, R. (2020, April). Comparison of different spatial interpolation techniques to thematic mapping of socio-economic causes of crime against women. In 2020 Systems and Information Engineering Design Symposium (SIEDS) (pp. 1–6). IEEE. https://doi.org/10.1109/SIEDS49339.2020.9106690

 Google Scholar

Reed, M. S. (2015). Predicting spatial patterns of identity theft victimization using overlay mapping [Doctoral dissertation]. San Diego State University.

 Google Scholar

Reinhart, A. (2016). Point process modeling with spatiotemporal covariates for predicting crime [Doctoral dissertation]. Carnegie Mellon University.

 Google Scholar

Ristea, A., Al Boni, M., Resch, B., Gerber, M. S., & Leitner, M. (2020). Spatial crime distribution and prediction for sporting events using social media. International Journal of Geographical Information Science: IJGIS, 34(9), 1708–1739. https://doi.org/10.1080/13658816.2020.1719495

 PubMed Web of Science ®Google Scholar

Rosés, R. (2020). Exploring theory-informed, data-driven simulations for predicting crime [Doctoral dissertation]. ETH Zurich.

 Google Scholar

Rosés, R., Kadar, C., & Malleson, N. (2021). A data-driven agent-based simulation to predict crime patterns in an urban environment. Computers, Environment and Urban Systems, 89, 101660. https://doi.org/10.1016/j.compenvurbsys.2021.101660

 Web of Science ®Google Scholar

Ruiz, D. R., & Sawant, A. (2019). Quantitative analysis of crime incidents in Chicago using data analytics techniques. Computers, Materials & Continua, 59(2), 389–396. https://doi.org/10.32604/cmc.2019.06433

 Web of Science ®Google Scholar

Rumi, S. K., & Salim, F. D. (2020, October). Modelling regional crime risk using directed graph of check-ins. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management (pp. 2201–2204). https://doi.org/10.1145/3340531.3412065

 Google Scholar

Rumi, S. K., Deng, K., & Salim, F. D. (2018, November). Theft prediction with individual risk factor of visitors. In Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (pp. 552–555). https://doi.org/10.1145/3274895.3274994

 Google Scholar

Rummens, A., & Hardyns, W. (2020). Comparison of near-repeat, machine learning, and risk terrain modeling for making spatiotemporal predictions of crime. Applied Spatial Analysis and Policy, 13(4), 1035–1053. https://doi.org/10.1007/s12061-020-09339-2

 Web of Science ®Google Scholar

Rummens, A., & Hardyns, W. (2021). The effect of spatiotemporal resolution on predictive policing model performance. International Journal of Forecasting, 37(1), 125–133. https://doi.org/10.1016/j.ijforecast.2020.03.006

 Web of Science ®Google Scholar

Rummens, A., Hardyns, W., & Pauwels, L. (2017). The use of predictive analysis in spatiotemporal crime forecasting: Building and testing a model in an urban context. Applied Geography, 86, 255–261. https://doi.org/10.1016/j.apgeog.2017.06.011

 Web of Science ®Google Scholar

Rummens, A., Snaphaan, T., Van de Weghe, N., Van den Poel, D., Pauwels, L. J. R., & Hardyns, W. (2021). Do mobile phone data provide a better denominator in crime rates and improve spatiotemporal predictions of crime? ISPRS International Journal of Geo-Information, 10(6), 369. https://doi.org/10.3390/ijgi10060369

 Web of Science ®Google Scholar

Salama, U., Chen, X., Yao, L., Paik, H. Y., & Wang, X. (2021). Deep multi-view spatio-temporal network for urban crime prediction. In Databases theory and applications: 32nd Australasian Database Conference, ADC 2021, Dunedin, New Zealand, January 29–February 5, 2021, Proceedings 32 (pp. 50–61). Springer International Publishing.

 Google Scholar

Schaffter, C. (2020). Utilizing geographic information systems to analyze emerging hotspots and cold spots of violent and non-violent crime. [Doctoral dissertation]. Utica College.

 Google Scholar

Shukla, A., Katal, A., Raghuvanshi, S., & Sharma, S. (2021, June). Criminal combat: Crime analysis and prediction using machine learning. In 2021 International Conference on Intelligent Technologies (CONIT) (pp. 1–5). IEEE. https://doi.org/10.1109/CONIT51480.2021.9498397

 Google Scholar

Solomon, A., Kertis, M., Shapira, B., & Rokach, L. (2022). A deep learning framework for predicting burglaries based on multiple contextual factors. Expert Systems with Applications, 199, 117042. https://doi.org/10.1016/j.eswa.2022.117042

 Web of Science ®Google Scholar

Sujatha, R., & Ezhilmaran, D. (2014). An adaptive method for analyzing and predicting the crime locations by means of AMABC and ARM. Journal of Theoretical and Applied Information Technology, 59(1), 45–56.

 Google Scholar

Sujatha, R., & Ezhilmaran, D. (2016). A new efficient SIF-based FCIL (SIF-FCIL) mining algorithm in predicting the crime locations. Journal of Experimental & Theoretical Artificial Intelligence, 28(3), 561–579. https://doi.org/10.1080/0952813X.2015.1020573

 Web of Science ®Google Scholar

Sun, J., Yue, M., Lin, Z., Yang, X., Nocera, L., Kahn, G., & Shahabi, C. (2021). CrimeForecaster: Crime prediction by exploiting the geographical neighborhoods’ spatiotemporal dependencies. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases (pp. 52–67).

 Google Scholar

Tang, Y., Zhu, X., Guo, W., Wu, L., & Fan, Y. (2019). Anisotropic diffusion for improved crime prediction in urban China. ISPRS International Journal of Geo-Information, 8(5), 234. https://doi.org/10.3390/ijgi8050234

 Web of Science ®Google Scholar

Tarlekar, S., Bhosle, R., D'souza, E., & Sheikh, S. (2021). August). Geographical crime rate prediction system. 2021 IEEE India Council International Subsections Conference (INDISCON) (pp. 1–6). IEEE. https://doi.org/10.1109/INDISCON53343.2021.9582218

 Google Scholar

Tavares, J. P., & Costa, A. C. (2021). Spatial modeling and analysis of the determinants of property crime in Portugal. ISPRS International Journal of Geo-Information, 10(11), 731. https://doi.org/10.3390/ijgi10110731

 Web of Science ®Google Scholar

Tian, Z., & Zhang, S. (2021). Application of big data optimized clustering algorithm in cloud computing environment in traffic accident forecast. Peer-to-Peer Networking and Applications, 14(4), 2511–2523. https://doi.org/10.1007/s12083-020-00994-3

 Web of Science ®Google Scholar

Vinnia Kemala Putri, & Felix, I. K. (2019). Crimes prediction using spatio-temporal data and kernel density estimation. 2019 Asia Pacific Conference on Research in Industrial and Systems Engineering (APCoRISE), Depok, Indonesia (pp. 1–6). https://doi.org/10.1109/APCoRISE46197.2019.9318972.

 Google Scholar

Vomfell, L., Härdle, W. K., & Lessmann, S. (2018). Improving crime count forecasts using Twitter and taxi data. Decision Support Systems, 113, 73–85. https://doi.org/10.1016/j.dss.2018.07.003

 Web of Science ®Google Scholar

Wang, X., & Brown, D. E. (2012). The spatio-temporal modeling for criminal incidents. Security Informatics, 1(1), 1–17. https://doi.org/10.1186/2190-8532-1-2

 Google Scholar

Wang, B., Yin, P., Bertozzi, A. L., Brantingham, P. J., Osher, S. J., & Xin, J. (2019). Deep learning for real-time crime forecasting and its ternarization. Chinese Annals of Mathematics, Series B, 40(6), 949–966. https://doi.org/10.1007/s11401-019-0168-y

 Web of Science ®Google Scholar

Wang, H., Yao, H., Kifer, D., Graif, C., & Li, Z. (2017). Non-stationary model for crime rate inference using modern urban data. IEEE Transactions on Big Data, 5(2), 180–194. https://doi.org/10.1109/TBDATA.2017.2786405

 PubMed Web of Science ®Google Scholar

Wang, J., Hu, J., Shen, S., Zhuang, J., & Ni, S. (2020). Crime risk analysis through big data algorithm with urban metrics. Physica A: Statistical Mechanics and Its Applications, 545, 123627. https://doi.org/10.1016/j.physa.2019.123627

 Web of Science ®Google Scholar

Wang, D., Ding, W., Lo, H., Morabito, M., Chen, P., Salazar, J., & Stepinski, T. (2013). Understanding the spatial distribution of crime based on its related variables using geospatial discriminative patterns. Computers, Environment and Urban Systems, 39, 93–106. https://doi.org/10.1016/j.compenvurbsys.2013.01.008

 Web of Science ®Google Scholar

Wang, X., Brown, D. E., & Gerber, M. S. (2012). Spatio-temporal modeling of criminal incidents using geographic, demographic, and Twitter-derived information. Paper presented at the ISI 2012 - 2012 IEEE International Conference on Intelligence and Security Informatics: Cyberspace, Border, and Immigration Securities.

 Google Scholar

Wawrzyniak, Z. M., Borowik, G., Szczechla, E., Michalak, P., Pytlak, R., Cichosz, P., … Perkowski, E. (2018). Relationships between crime and everyday factors. Paper presented at the INES 2018 - IEEE 22nd International Conference on Intelligent Engineering Systems, Proceedings.

 Google Scholar

Wei, Y., Liang, W., Wang, Y., & Cao, J. (2020, November). CrimeSTC: A deep spatial-temporal-categorical network for citywide crime prediction. In Proceedings of the 2020 3rd International Conference on Computational Intelligence and Intelligent Systems (pp. 75–79). https://doi.org/10.1145/3440840.3440850

 Google Scholar

Wheeler, A. P., & Steenbeek, W. (2021). Mapping the risk terrain for crime using machine learning. Journal of Quantitative Criminology, 37(2), 445–480.

 Web of Science ®Google Scholar

Wu, J., Hui, J., & Xian, R. (2017). Utilization of street view and satellite imagery data for crime prediction. Presentation CS230 at Stanford University.

 Google Scholar

Xia, Z., Stewart, K., & Fan, J. (2021). Incorporating space and time into random forest models for analyzing geospatial patterns of drug-related crime incidents in a major U.S. metropolitan area. Computers, Environment and Urban Systems, 87, 101599. https://doi.org/10.1016/j.compenvurbsys.2021.101599

 PubMed Web of Science ®Google Scholar

Yang, D., Heaney, T., Tonon, A., Wang, L., & Cudré-Mauroux, P. (2018). CrimeTelescope: Crime hotspot prediction based on urban and social media data fusion. World Wide Web, 21(5), 1323–1347. https://doi.org/10.1007/s11280-017-0515-4

 Google Scholar

Yang, B., Liu, L., Lan, M., Wang, Z., Zhou, H., & Yu, H. (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), 1740–1764. https://doi.org/10.1080/13658816.2020.1737701

 Web of Science ®Google Scholar

Yao, S., Wei, M., Yan, L., Wang, C., Dong, X., Liu, F., & Xiong, Y. (2020, August). Prediction of crime hotspots based on spatial factors of random forest. In 2020 15th International Conference on Computer Science & Education (ICCSE) (pp. 811–815). IEEE. https://doi.org/10.1109/ICCSE49874.2020.9201899

 Google Scholar

Ye, X., Duan, L., & Peng, Q. (2021). Spatiotemporal prediction of theft risk with deep inception-residual networks. Smart Cities, 4(1), 204–216. https://doi.org/10.3390/smartcities4010013

 Google Scholar

Yi, F., Yu, Z., Zhuang, F., Zhang, X., & Xiong, H. (2018, November). An integrated model for crime prediction using temporal and spatial factors. In 2018 IEEE International Conference on Data Mining (ICDM) (pp. 1386–1391). IEEE. https://doi.org/10.1109/ICDM.2018.00190

 Google Scholar

Yoo, Y., & Wheeler, A. P. (2019). Using risk terrain modeling to predict homeless related crime in Los Angeles, California. Applied Geography, 109, 102039.

 Web of Science ®Google Scholar

Yuan, Z., Zhou, X., & Yang, T. (2018, July) Hetero-convlstm: A deep learning approach to traffic accident prediction on heterogeneous spatio-temporal data. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 984–992).

 Google Scholar

Zhang, Y., Siriaraya, P., Kawai, Y., & Jatowt, A. (2019). Time and location recommendation for crime prevention. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11496 LNCS (pp. 47–62).

 Google Scholar

Zhang, Y., Siriaraya, P., Kawai, Y., & Jatowt, A. (2020a). Analysis of street crime predictors in web open data. Journal of Intelligent Information Systems, 55(3), 535–559. https://doi.org/10.1007/s10844-019-00587-4

 Web of Science ®Google Scholar

Zhang, Y., Siriaraya, P., Kawai, Y., & Jatowt, A. (2020b). Predicting time and location of future crimes with recommendation methods. Knowledge-Based Systems, 210, 106503. https://doi.org/10.1016/j.knosys.2020.106503

 Web of Science ®Google Scholar

Zhang, H., Zhang, J., Wang, Z., & Yin, H. (2021). An adaptive spatial resolution method based on the ST-ResNet model for hourly property crime prediction. ISPRS International Journal of Geo-Information, 10(5), 314. https://doi.org/10.3390/ijgi10050314

 Web of Science ®Google Scholar

Zhang, X., Liu, L., Lan, M., Song, G., Xiao, L., & Chen, J. (2022). Interpretable machine learning models for crime prediction. Computers, Environment and Urban Systems, 94, 101789. https://doi.org/10.1016/j.compenvurbsys.2022.101789

 Web of Science ®Google Scholar

Zhao, X., & Tang, J. (2017, November). Modeling temporal-spatial correlations for crime prediction. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management (pp. 497–506). https://doi.org/10.1145/3132847.3133024

 Google Scholar

Zheng, Z., Xia, Y., Chen, X., & Yao, J. (2023). Security alert: Generalized deep multi‐view representation learning for crime forecasting. Computational Intelligence, 39(1), 4–17. https://doi.org/10.1111/coin.12504

 Web of Science ®Google Scholar

Zhou, J., Li, Z., Ma, J. J., & Jiang, F. (2020). Exploration of the hidden influential factors on crime activities: A big data approach. IEEE Access, 8, 141033–141045. https://doi.org/10.1109/ACCESS.2020.3009969

 Web of Science ®Google Scholar

Zhou, B. B., Chen, L. B., Zhou, F. X., Li, S. J., Zhao, S., Das, S. K., & Pan, G. (2021). ESCORT: Fine-grained urban crime risk inference leveraging heterogeneous open data. IEEE Systems Journal, 15(3), 4656–4667. https://doi.org/10.1109/JSYST.2020.3023762

 Web of Science ®Google Scholar

Zhou, B. B., Chen, L. B., Zhou, F. X., Li, S. J., Zhao, S., & Pan, G. (2022). Dynamic road crime risk prediction with urban open data. Frontiers of Computer Science, 16(1), 1–13. https://doi.org/10.1007/s11704-021-0136-z

 Web of Science ®Google Scholar

Zhu, Y. (2018). Comparison of model performance for basic and advanced modeling approaches to crime prediction. Intelligent Information Management, 10(06), 123–132. https://doi.org/10.4236/iim.2018.106011

 Google Scholar

Zuniga-Garcia, N., Perrine, K. A., & Kockelman, K. M. (2022). Predicting pedestrian crashes in Texas’ intersections and midblock segments. Sustainability (Switzerland), 14(12), 7164. https://doi.org/10.3390/su14127164

 Web of Science ®Google Scholar