Dataset Information

Blowing in the wind? Testing the effect of weather on the spatial distribution of crime using Generalized Additive Models.

ABSTRACT: Oslo, the capital of Norway, is situated in a North European cool climate zone. We investigate the effect of weather on the overall level of crime in the city, as well as the impact of different aspects of weather (temperature, wind speed, precipitation) on the spatial distribution of crime, net of both total level of crime, time of day and seasonality. Geocoded locations of criminal offences were combined with data on temperature, wind speed, and precipitation. Generalized Additive Models (GAMs) allowed us to map level of and the spatial distribution of crime, and how it was impacted by weather, in a more robust manner than in previous studies. There was slightly more crime in pleasurable weather (i.e. low precipitation and wind speed and high temperatures). However, neither temperature, precipitation nor wind speed impacted the spatial distribution of crime in the city.

Supplementary information

The online version contains supplementary material available at 10.1186/s40163-022-00171-2.

SUBMITTER: Hart R

PROVIDER: S-EPMC9525942 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Blowing in the wind? Testing the effect of weather on the spatial distribution of crime using Generalized Additive Models.

Hart Rannveig R Pedersen Willy W Skardhamar Torbjørn T

Crime science 20221001 1

Oslo, the capital of Norway, is situated in a North European cool climate zone. We investigate the effect of weather on the overall level of crime in the city, as well as the impact of different aspects of weather (temperature, wind speed, precipitation) on the spatial distribution of crime, net of both total level of crime, time of day and seasonality. Geocoded locations of criminal offences were combined with data on temperature, wind speed, and precipitation. Generalized Additive Models (GAMs ...[more]

PMID: 36211474

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Project description:BackgroundA common, important problem in spatial epidemiology is measuring and identifying variation in disease risk across a study region. In application of statistical methods, the problem has two parts. First, spatial variation in risk must be detected across the study region and, second, areas of increased or decreased risk must be correctly identified. The location of such areas may give clues to environmental sources of exposure and disease etiology. One statistical method applicable in spatial epidemiologic settings is a generalized additive model (GAM) which can be applied with a bivariate LOESS smoother to account for geographic location as a possible predictor of disease status. A natural hypothesis when applying this method is whether residential location of subjects is associated with the outcome, i.e. is the smoothing term necessary? Permutation tests are a reasonable hypothesis testing method and provide adequate power under a simple alternative hypothesis. These tests have yet to be compared to other spatial statistics.ResultsThis research uses simulated point data generated under three alternative hypotheses to evaluate the properties of the permutation methods and compare them to the popular spatial scan statistic in a case-control setting. Case 1 was a single circular cluster centered in a circular study region. The spatial scan statistic had the highest power though the GAM method estimates did not fall far behind. Case 2 was a single point source located at the center of a circular cluster and Case 3 was a line source at the center of the horizontal axis of a square study region. Each had linearly decreasing logodds with distance from the point. The GAM methods outperformed the scan statistic in Cases 2 and 3. Comparing sensitivity, measured as the proportion of the exposure source correctly identified as high or low risk, the GAM methods outperformed the scan statistic in all three Cases.ConclusionsThe GAM permutation testing methods provide a regression-based alternative to the spatial scan statistic. Across all hypotheses examined in this research, the GAM methods had competing or greater power estimates and sensitivities exceeding that of the spatial scan statistic.

Dataset Information

Blowing in the wind? Testing the effect of weather on the spatial distribution of crime using Generalized Additive Models.

Supplementary information

Publications

Blowing in the wind? Testing the effect of weather on the spatial distribution of crime using Generalized Additive Models.

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