Version 1
: Received: 13 January 2021 / Approved: 15 January 2021 / Online: 15 January 2021 (12:47:45 CET)
How to cite:
Lau, H.Y. Discovering Spatio-Temporal Pattern of City Crime – Visual Analysis on Felony Crime in New York. Preprints2021, 2021010292. https://doi.org/10.20944/preprints202101.0292.v1
Lau, H.Y. Discovering Spatio-Temporal Pattern of City Crime – Visual Analysis on Felony Crime in New York. Preprints 2021, 2021010292. https://doi.org/10.20944/preprints202101.0292.v1
Lau, H.Y. Discovering Spatio-Temporal Pattern of City Crime – Visual Analysis on Felony Crime in New York. Preprints2021, 2021010292. https://doi.org/10.20944/preprints202101.0292.v1
APA Style
Lau, H.Y. (2021). Discovering Spatio-Temporal Pattern of City Crime – Visual Analysis on Felony Crime in New York. Preprints. https://doi.org/10.20944/preprints202101.0292.v1
Chicago/Turabian Style
Lau, H.Y. 2021 "Discovering Spatio-Temporal Pattern of City Crime – Visual Analysis on Felony Crime in New York" Preprints. https://doi.org/10.20944/preprints202101.0292.v1
Abstract
Pattern recognition has long been regarded as key role for crime prevention and reduction. Crime analysts and policy makers can formulate effective strategies and allocate resources with reference to spatial and temporal pattern of crime. In order the combat and prevent severe crime in New York City (NYC), this study analyzed Felony Crime data of NYC in previous 5 years (2015 2020) and discovered criminal hotspots pattern and temporal patterns with open criminal complaint data provided by New York Police Department (NYPD). This study adapt a human computer interactive appraoch to draw patterns from crime data, whereas computations and visualization are performed by Python libraries, and human to inform the decision of visualization methods, computational parameters and direction of this exploratary analysis. Density based clustering algorithms, Grid Thematic Mapping and Density Heatmap are displayed to identify hotspots and demonstrates their associations with spatial features. Timeline analysis on moments of crime occurance demonstrates seasonality where crimes are mostly commited, while aoristic analysis showed hours of day when crime is mostly committed considering their timespan. Lastly, 3D visualization improved recognition of the displacement of hotspot over time, and suggested long term hotspots in NYC in 3 D visualization. This inform strategic plans for police deployment.
crime; hotspots; Space-Time clustering; New York; Visual analytics
Subject
Computer Science and Mathematics, Algebra and Number Theory
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
