Using remote sensing data to derive built-form indexes to analyze the geography of residential burglary and street thefts

• By deploying remotely sensed data together with spatial statistical modeling, we use regression modeling to investigate the relationship between the density of the built environment and two types of crime. We show how the Global Human Settlement Layer (GHSL) data set, which is a measure of building density generated from Sentinel 2A satellite imagery, can be used to create different indexes to describe the built environment for the purpose of analyzing crime patterns for indoor crimes (residential burglary) and open space crimes (street theft). Analysis is at neighborhood level for Stockholm, Sweden. Modeling is then extended to incorporate six planning areas which represent different neighborhood types within the city. Modeling is further extended by adding selected social, economic, demographic and land use variables that have been found to be significant in explaining spatial variation in the two crime categories in Stockholm. Significant associations between the GHSL-based indexes and the two crime rates are observed but results indicate that allowance for differences in neighborhood type should be recognized. Average income and transport hubs were also significant variables in the investigated crime categories. The article provides a practical demonstration and assessment of the use of high-resolution satellite data to examine the association between urban density and two common types of crime and offers reflections about the use of satellite image data in crime analysis.

Ämnesord

HUMANIORA  -- Konst -- Arkitektur (hsv//swe)

HUMANITIES  -- Arts -- Architecture (hsv//eng)

Nyckelord

Urban and Regional Studies

Urbana och regionala studier

Urban Design

Stadsbyggnad

Geoinformatik

Geoinformatics

Risk and Safety

Risk och säkerhet

Publikations- och innehållstyp

ref (ämneskategori)

art (ämneskategori)