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Putting spatial crime patterns in their social contexts through a contextualized colocation analysis

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Abstract

This study proposes a novel contextualized colocation analysis to examine spatial crime patterns within their social contexts. The sample includes all reported MCI crime incidents (i.e., assault, break and enter, robbery, auto theft, and theft over incidents) in the city of Toronto between 2014 and 2019 (n = 178,892). Following a stepwise clustering feature selection, we begin our analysis by regionalizing the city based on the relevant social context indicators through a ward-like hierarchical spatial clustering algorithm. Then, we use a modified colocation miner algorithm with a novel Validity Score (VS) to select significant citywide and regional crime colocation patterns. The results indicate that eating establishments, commercial parking lots, and retail food stores are the most frequent urban facilities in citywide and regional crime colocation patterns. We also note several peculiar crime colocation patterns across disadvantaged neighborhoods. Additionally, the proposed analysis selects the patterns that explain an average of 11% more crime events through the use of VS. Our study offers an alternative method for colocation analysis by effectively identifying crime-specific citywide and regional crime colocation patterns. It also prioritizes the identified colocation patterns by ranking them based on their significance.

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Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

References

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Funding

The authors did not receive support from any organization for the submitted work.

Author information

Authors and Affiliations

  1. Department of Management Information Systems, Istanbul Bilgi University, Istanbul, Turkey

    Tugrul Cabir Hakyemez

  2. Data Science Laboratory, Toronto Metropolitan University, 44 Gerrard Street East, Toronto, ON, M5B 1G3, Canada

    Ceni Babaoglu & Ayse Basar

Authors
  1. Tugrul Cabir Hakyemez
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  2. Ceni Babaoglu
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  3. Ayse Basar
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Contributions

TCH: Conceptualization, Methodology, Data curation, Formal Analysis, Writing—Original draft preparation, Visualization, Investigation, Writing—Review & Editing. CB: Conceptualization, Methodology, Writing- Original draft preparation, Investigation, Supervision. AB: Supervision.

Corresponding author

Correspondence to Tugrul Cabir Hakyemez.

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The authors have no competing interests to declare that are relevant to the content of this article.

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This article does not contain any studies with human participants performed by any of the authors.

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Appendices

Appendix A. A sample neighborhood list

ObjectID

Neighboring object IDs

1

(36490, 36509, 514064)

2

(7, 437, 440, 36508, 51316, 97822)

Appendix B. A Boxplots for (a) LDI (b) MHI (c) RFCR (d)RIR (e) RR (f) UR

Note: The boxplot of the first cluster was colored with gray, instead of black, to highlight the median line.

figure a

Appendix C. The results of contextualized crime colocation analysis

Abbreviations in the results table: Adult Entertainment (AE), Cafe(C), Market(M), Commercial Parking Lots (CPL), Laundry(L), Nightclub (NC), Pawnbroker (PB), Place of Amusement (PoA), Precious Metal Shop (PMS), Public Hall (PH), Retail Food Store (RFS), Theater(T), and Eating Establishment (EE), Community Houses (CH), Police Stations (PS), Worship Places (WP), and Schools (S).

Appendix C.1 Top 5 CPs for Assault

CP

Citywide

VS

EN

VS

DN

VS

DevN

VS

1

{CPL}

0.758

{CPL}(−)*

0.918

{CPL}

0.966

{CPL} (−)*

0.966

2

{RFS}

0.500

{EE,RFS}

0.817

{EE, RFS}

0.943

{EE, RFS}(+)**

0.943

3

{BC, EE}

0.382

{EE}(−)*

0.734

{EE, L,RFS}

0.593

{EE}(−)*

0.928

4

{EE} (−)**

0.275

{EE, L}

0.559

{EE,L}

0.589

{EE, L, RFS}

0.593

5

{CPL, EE, RSF}

0.279

{EE, L, RFS}

0.556

{EE}(−)*

0.570

{EE, L}

0.589

  1. Note: (+): Positive association, (−): Negative association, (*) Significant at α = 0.05 value (**) Significant at α = 0.01 value

Appendix C.2 Top 5 CPs for Auto Theft

CP

Citywide

VS

EN

VS

DN

VS

DevN

VS

1

{EE, RFS}

0.615

{EE, RFS}

0.643

{EE,RFS}

0.542

{EE, RFS}

0.888

2

{RFS}

0513

{EE}(−)*

0.627

{EE,L}

0.533

{S}

0.625

3

{EE, L}

0.444

{RFS}

0.591

{EE,L,RFS}

0.532

{EE, L} (−)*

0.593

4

{EE}(−)*

0.443

{EE, L}

0.401

{RFS}

0.400

{EE, L, RFS}

0.593

5

{EE, L, RFS}

0.441

{EE, L, RFS}

0.397

{EE, RFS,WP}

0.386

{EE}(−)*

0.570

  1. Note: (+): Positive association, (−): Negative association, (*) Significant at α = 0.05 value (**) Significant at α = 0.01 value

Appendix C.3 Top 5 CPs for Break and Enter

CP

Citywide

VS

EN

VS

DN

VS

DevN

VS

1

{EE, RFS} (−)**

0,665

{EE, RFS} (−)*

0,720

{EE, RFS} (−)*

0.718

{EE}(−)**

0,781

2

{EE, L} (−)**

0,520

{EE}(−)**

0,613

{EE, L} (−)*

0,515

{EE, RFS} (−)*

0,754

3

{EE, L, RF} (−)*

0,518

{RFS}

0,590

{EE, L, RFS} (−)*

0,513

{RFS}

0,734

4

{RFS}

0,488

{EE, L} (−)*

0,550

{EE, RFS, WP} (−)**

0,254

{CPL} (−)*

0,524

5

{EE, RFS, WP}

0,398

{EE, L, RFS}

0,543

{S}

0,252

{CPL, EE} (−)*

0,512

  1. Note: (+): Positive association, (−): Negative association, (*) Significant at α = 0.05 value (**) Significant at α = 0.01 value

Appendix C.4 Top 5 CPs for Robbery

CP

Citywide

VS

EN

VS

DN

VS

DevN

VS

1

{EE, RFS} (+)**

0.671

{EE, RFS} (−)**

0.472

{EE, L, RFS} (+)*

0.528

{CPL} (+)**

0.786

2

{EE, L, RFS} (+)**

0.543

{EE, L} (+)**

0.445

{EE, L} (+)**

0.499

{CPL, EE, RFS}

0.741

3

{EE, L} (+)**

0.536

{EE, L, RFS} (+)**

0.428

{C} (+)**

0.457

{EE, L} (+)**

0.536

4

{EE} (+)**

0.459

{CPL, EE, RFS}

0.396

{EE} (+)**

0.423

{EE, L, RFS} (+)*

0.490

5

{CPL, EE, RFS} (+)*

0.378

{CPL, EE} (+)*

0.389

{CPL, EE, RFS}

0.334

{EE, RFS} (+)*

0.484

  1. Note: (+): Positive association, (−): Negative association, (*) Significant at α = 0.05 value (**) Significant at α = 0.01 value

Appendix C.5 Top 5 CPs for Theft Over

CP

Citywide

VS

EN

VS

DN

VS

DevN

VS

1

{CPL, EE, RFS} (+)*

0.437

{CPL} (+)**

0.459

{EE, RFS}(+)*

0.627

{EE, RFS} (+)**

1

2

{CPL, EE} (+)**

0.430

{CPL, EE} (+)**

0.451

{EE}(+)**

0.603

{EE} (+)**

1

3

{CPL} (+)**

0.409

{CPL, EE, RFS} (+)**

0.437

{CPL, EE, RFS} (+)**

0.515

{CPL, EE, RFS} (+)*

1

4

{EE, L, RFS} (+)*

0.306

{EE, RFS} (+)*

0.343

{CPL, EE} (+)**

0.514

{CPL, EE} (+)**

0.627

5

{EE, L} (+)**

0.262

{EE, L, RFS} (+)*

0.342

{CPL} (+)**

0.511

{CPL} (+)**

0.603

  1. Note: (+): Positive association, (−): Negative association, (*) Significant at α = 0.05 value (**) Significant at α = 0.01 value

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Hakyemez, T.C., Babaoglu, C. & Basar, A. Putting spatial crime patterns in their social contexts through a contextualized colocation analysis. GeoJournal 88, 5721–5741 (2023). https://doi.org/10.1007/s10708-023-10931-5

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