CrimeStat: A Spatial Statistical Program for the Analysis of Crime Incidents

Levine, Ned

doi:10.1007/978-0-387-35973-1_229

Cited by 156 publications

(284 citation statements)

References 7 publications

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“…a representative location for each user). The spatial median is defined here as the intersection between the median of the X coordinates and the median of the Y coordinates (Levine, 2013). For each user, we ordered the X coordinates and the Y coordinates and chose the median of the X and the median of the Y coordinates.…”

Section: Modeling Population At Riskmentioning

confidence: 99%

Population at risk: using areal interpolation and Twitter messages to create population models for burglaries and robberies

Kounadi

Ristea

Leitner

et al. 2017

Cartography and Geographic Information Science

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Population at risk of crime varies due to the characteristics of a population as well as the crime generator and attractor places where crime is located. This establishes different crime opportunities for different crimes. However, there are very few efforts of modeling structures that derive spatiotemporal population models to allow accurate assessment of population exposure to crime. This study develops population models to depict the spatial distribution of people who have a heightened crime risk for burglaries and robberies. The data used in the study include: Census data as source data for the existing population, Twitter geo-located data, and locations of schools as ancillary data to redistribute the source data more accurately in the space, and finally gridded population and crime data to evaluate the derived population models. To create the models, a density-weighted areal interpolation technique was used that disaggregates the source data in smaller spatial units considering the spatial distribution of the ancillary data. The models were evaluated with validation data that assess the interpolation error and spatial statistics that examine their relationship with the crime types. Our approach derived population models of a finer resolution that can assist in more precise spatial crime analyses and also provide accurate information about crime rates to the public.

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Section: Modeling Population At Riskmentioning

confidence: 99%

Population at risk: using areal interpolation and Twitter messages to create population models for burglaries and robberies

Kounadi

Ristea

Leitner

et al. 2017

Cartography and Geographic Information Science

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“…We used the Ripley's K statistics (15) to evaluate the non-random distribution of roadkills. Although the road linearization causes loss of dimensionality, with this transformation the formation of false clusters at the curves was prevented (14).…”

Section: Identificación Molecular De Las Especiesmentioning

confidence: 99%

Roadkill hotspots in a protected area of Cerrado in Brazil: planning actions to conservation

et al. 2016

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Objective. Here we aimed to identify the main points of animal death by roadkill in the view of helping mitigation plans and reducing the impact over the local fauna of a protected area. Materials and methods. We surveyed the roads around a protected area of Cerrado (São Paulo, Brazil) from May 2012 to August 2013. We recorded the local of roadkills, biometric and morphologic data of the animals, and collected samples of tissue for molecular species confirmation. Results. Thirty-one roadkilled animals were registered, including threatened species: Leopardus pardalis; Cuniculus paca and Chrysocyon brachyurus. Most roadkills were represented by mammals (54.8%) and reptiles (38.7%), and the mortality rate was 1.46 animals/km/year. Three roadkill hotspots were detected, suggesting that they were important points of animal crossing, probably because of the existence of natural remnant vegetation and intersection of roads by riparian vegetation. Conclusions. This work provided strong evidence of the most critical points where mitigation strategies should be immediately implemented and highlighted the importance of detecting roadkill hotspots and the species or taxonomic groups more affected, helping to elaborate effective actions that can improve fauna conservation.

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“…2). The local Moran's I index can examine every individual location and the results can be standardized, so its significance level can be tested based on an assumption of a normal distribution (Anselin, 1995;Levine, 2004). However, the normality assumption for the index may not be appropriate, especially when the raw data is heavily skewed.…”

Section: Spatial-cluster and Spatial-outlier Analysesmentioning

confidence: 99%

Spatial variation of soil test phosphorus in a long‐term grazed experimental grassland field

Tunney

Zhang

2010

Z. Pflanzenernähr. Bodenk.

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The spatial variation of soil test P (STP) in grassland soils is becoming important because of the use of STP as a basis for policies such as the recently EU-introduced Nitrate Directive. This research investigates the spatial variation of soil P in grazed grassland plots with a long-term (38 y) experiment. A total of 326 soil samples (including 14 samples from an adjacent grasswood buffer zone) were collected based on a 10 × 10 m 2 grid system. The samples were measured for STP and other nutrients. The results were analyzed using conventional statistics, geostatistics, and a geographic information system (GIS). Soil test P concentrations followed a lognormal distribution, with a median of 5.30 mg L -1 and a geometric mean of 5.35 mg L -1 . Statistically significant (p < 0.01) positive correlation between STP and pH was found. Spatial clusters and spatial outliers were detected using the local Moran's I index (a local indicator of spatial association) and were mapped using GIS. An obvious low-value spatial-cluster area was observed on the plots that received zero-P fertilizer application from 1968 to 1998 and a large high-value spatial-cluster area was found on the relatively high-P fertilizer application plots (15 kg ha -1 y -1 ). The local Moran's I index was also effective in detecting spatial outliers, especially at locations close to spatial-cluster areas. To obtain a reliable and stable spatial structure, semivariogram of soil-P data was produced after elimination of spatial outliers. A spherical model with a nugget effect was chosen to fit the experimental semivariogram. The spatial-distribution map of soil P was produced using the kriging interpolation method. The interpolated distribution map was dominated by medium STP values, ranging from 3 mg to 8 mg L -1 . An evidently low-P-value area was present in the upper side of the study area, as zero or short-term P fertilizer was applied on the plots. Meanwhile, high-P-value area was located mainly on the plots receiving 15 kg P ha -1 y -1 (for 38 y) as these plots accumulated excess P after a long-term P-fertilizer spreading. The high-or low-value patterns were in line with the spatial clusters. Geostatistics, combined with GIS and the local spatial autocorrelation index, provides a useful tool for analyzing the spatial variation in soil nutrients.

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CrimeStat: A Spatial Statistical Program for the Analysis of Crime Incidents

Cited by 156 publications

References 7 publications

Population at risk: using areal interpolation and Twitter messages to create population models for burglaries and robberies

Population at risk: using areal interpolation and Twitter messages to create population models for burglaries and robberies

Roadkill hotspots in a protected area of Cerrado in Brazil: planning actions to conservation

Spatial variation of soil test phosphorus in a long‐term grazed experimental grassland field

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