Mapping crime harm and priority locations in New Zealand: A comparison of spatial analysis methods

Curtis‐Ham, Sophie; Walton, Darren

doi:10.1016/j.apgeog.2017.06.008

Cited by 33 publications

(32 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The study analyzes the factors between the crime damage index and the priority locations index in communities and neighborhoods, having higher levels of crime. The benefits brought by this research allow the optimization of resources of the police and of the agencies responsible of reduce crime [15]. The analysis of previous works, let us affirm that in the last few years the development of applications using interactive maps for data georeferencing data is highly relevant and vital for strengthening the practices related to e-government, e-collaboration and e-democracy.…”

Section: Introductionmentioning

confidence: 60%

“…18% comes from other cities. The presented literature from [14][15][16][17] demonstrates the functionality of TAM as a tool widely used by several researchers and authors of relevant works. The investigations presented in [11] and [12] allow us to appreciate the use of GPS to generate operational maps in emergencies such as earthquakes; this served as a basis for this research.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Interpreting Felony Acts Using Georeferenced Data. Case Study in Ambato, Ecuador

Castillo¹,

Naranjo-Ávalos²,

Buele³

et al. 2021

Communications in Computer and Information Science

View full text Add to dashboard Cite

Georeferenced tools have made it possible to use the information collected through e-collaboration environments in a better way. Nevertheless, no study has been found on quantifying the influence of software tools over the capacity of interpreting data of victims of some kind of felony. This paper aims to validate the technology acceptance of applications georeferencing crimes using a prototype that could be useful for certain institutions to generate public policies. A web prototype is implemented to georeference data provided by felony acts victims. The application's prototype was validated by a group of 122 college students using the Technology Acceptance Model. The results were interpreted with the help of Kendall Tau-b correlation analysis where highly significant positive correlation values were obtained.

show abstract

Section: Introductionmentioning

confidence: 60%

Section: Discussionmentioning

confidence: 99%

Interpreting Felony Acts Using Georeferenced Data. Case Study in Ambato, Ecuador

Castillo¹,

Naranjo-Ávalos²,

Buele³

et al. 2021

Communications in Computer and Information Science

View full text Add to dashboard Cite

show abstract

“…The third novel contribution of the study is to evaluate the predictive accuracy of our identified cost of crime weighted hot spots using historical data to predict future crimes. While prior work has mapped harm spots (Curtis-Ham & Walton, 2017;Fenimore, 2019;Norton et al, 2018;Weinborn et al, 2017), the majority of that work has simply focused on crime concentration, and has not assessed the predictive accuracy of a technique to create such harmspots (for an exception, see Macbeth & Ariel, 2019). Additionally, most of that work has mapped harm spots at a specific pre-chosen unit of analysis (Curtis-Ham & Walton, 2017;Mitchell, 2019;Norton et al, 2018;Weinborn et al, 2018, for an exception see Fenimore, 2019 who used weighted kernel density estimates).…”

Section: Introductionmentioning

confidence: 99%

Redrawing Hot Spots of Crime in Dallas, Texas

Wheeler¹,

Reuter

2020

Police Quarterly

View full text Add to dashboard Cite

In this work we evaluate the predictive capability of identifying long term, micro place hot spots in Dallas, Texas. We create hot spots using a clustering algorithm, using law enforcement cost of responding to crime estimates as weights. Relative to the much larger current hot spot areas defined by the Dallas Police Department, our identified hot spots are much smaller (under 3 square miles), and capture crime cost at a higher density. We also show that the clustering algorithm captures a wide array of hot spot types; some one or two addresses, some street segments, and others an agglomeration of larger areas. This suggests identifying hot spots based on a specific unit of aggregation (e.g. addresses, street segments), may be less efficient than using a clustering technique in practice.

show abstract

“…These patterns can be useful for predicting crimes before they occur and allow the police to take proactive actions [8]. Geo-based crime analysis tools used by communities for areas that are vulnerable to crime problems [9]. Geographical mapping can provide insights about handling crime in cities, allocating resources to law enforcement agencies, and improving the environment that is built to prevent crime [10].…”

Section: Introductionmentioning

confidence: 99%

Implementation of K-Means Clustering and Weighted Products in Determining Crime-Prone Locations

Rahmatika

Sediyono

Widodo

2020

KINETIK

View full text Add to dashboard Cite

Crime is an act that violates the law. The number of criminal acts that occur becomes a social problem that makes the community and the police uneasy. Increasing the number of crimes is a problem in the social aspect. This research aims to build an information system to provide information on areas prone to a crime that can help the police to speed up the crime resolution process. The grouping process uses the k-means method used to classify based on the level of vulnerability of the area, grouping crime is a good strategy in improving crime prevention planning. In addition to the k-means clustering method, we also use the weighted product method which functions as a recommendation ranking for crime selection. The grouping results obtained from this study are that there is 1 very vulnerable area, 5 areas in the vulnerable category and 3 safe areas. While the weighted product method produces melatilor kudus city 'as a prone to beating areas with a score of 0.182093.

show abstract

Mapping crime harm and priority locations in New Zealand: A comparison of spatial analysis methods

Cited by 33 publications

References 20 publications

Interpreting Felony Acts Using Georeferenced Data. Case Study in Ambato, Ecuador

Interpreting Felony Acts Using Georeferenced Data. Case Study in Ambato, Ecuador

Redrawing Hot Spots of Crime in Dallas, Texas

Implementation of K-Means Clustering and Weighted Products in Determining Crime-Prone Locations

Contact Info

Product

Resources

About