Space Time Lab News Publication
- SpaceTimeLab Launch Event
- Public Crime Mapping Survey
- SpaceTimeLab at Geocomputation 2013
- Congratulations to Berk Anbaroglu and Ed Manley
- SpaceTimeLab welcomes new members
- Job Opportunity at SpaceTimeLab
- Congratulations to James Haworth
- Geospatial Seminar Series - Monsuru Adepeju
- Geospatial Seminar Series - Visualisation of traffic in space-time
- SpaceTimeLab Director Prof. Tao Cheng talks at ASU
- GIS Ostrava 2014
- Geospatial Seminar Series - Prof. Bin Jiang to talk at SpaceTimeLab
- Congratulations to Adel Bolbol
- SpaceTimeLab travels to AAG
- SpaceTimeLab researcher Sarah Wise beats competition to win best paper award at GISRUK 2014
- PhD Studenship Vacancy in Orbital Debris Modelling
- SpaceTimeLab for Big Data Analytics Newsletter 2014
- SpaceTimeLab research top of the pops!
- Juntao Lai wins best paper by a young researcher at GISRUK 2015
- Prof. John Shi on Spatial Data Science
- Prof. Tao Cheng at Evolving GIScience
- SpaceTimeLab Leaflet 2016
- SpaceTimeLab welcomes visitors from Didi and the Shanghai Government
- SpaceTimeLab's new mission statement
- Big Data for Intelligent Policing
- CPC Closing Workshop - Big Data and Intelligent Policing
- Big Data and Human Dynamics
- UCL SpaceTimeLab in 2017 New Scientist Live
- Geospatial Seminar Series: Demographic Prediction based on Traffic Smart Card Data
- SpaceTimeLab wins Shanghai Open Data Apps (SODA) Future Star Award
SpaceTimeLab wins Shanghai Open Data Apps (SODA) Future Star Award
Published: Nov 23, 2017 5:14:26 PM
Geospatial Seminar Series: Demographic Prediction based on Traffic Smart Card Data
Published: Oct 30, 2017 11:53:26 AM
Geospatial Seminar Series - Monsuru Adepeju
2 December 2013
On the 19th November, Monsuru Adepeju of SpaceTimeLab presented his recent research at the Geospatial Science Seminar Series in the UCL Geography Department. Details are as follows:
Title
Space-Time Pattern Analysis of Big Crime Datasets: Hotspots Detection for Predictive Policing
Abstract
Predicting where and when crime is likely to occur is an important aspect of police operation, especially toward the end of effective deployment of limited police resources. Crime hotspot detection is generally used, relying on historical crime datasets, to produce patterns of crime over large temporal steps such as monthly or seasonally. These patterns are then used to project the locations where crimes are likely to occur in near future at the same or larger scales. However, the use of large temporal scales are not suitable for proactive day-to-day crime intervention, especially at the local level. In this study, we combine both the global and local approach of hotspots detection to proactively identify areas of consistently high crime concentration at a finer temporal scale, i.e. daily, over a certain period of time. This approach provides better visualisation and profiling of crime patterns for better predictive policing.
Page last modified on 02 dec 13 11:09