We examined published maps containing sensitive data, and the protection methods, if any, that were used. We investigated whether the many published warnings about disclosure risk have been effective in reducing privacy risk. During an 8-year period (2005-2012), 19 journals related to GIScience, geography, spatial crime analysis, and health geography were examined. We identified 41 articles that display actual confidential information and 16 articles where confidential information is protected by the use of a geographical mask. During the investigated time frame, the numbers of articles with unmasked confidential data increased, and in total more than 68,000 home addresses were disclosed. One of the more significant findings of this study is that efforts to instill sensitivity to location privacy and disclosure risk have been relatively unsuccessful.

Participatory sensing applications collect personal data of monitored subjects along with their spatial or spatiotemporal stamps. The attributes of a monitored subject can be private, sensitive, or confidential information. Also, the spatial or spatiotemporal attributes are prone to inferential disclosure of private information. Although there is extensive problem-oriented literature on geoinformation disclosure, our work provides a clear guideline with practical relevance, containing the steps that a research campaign should follow to preserve the participants’ privacy. We first examine the technical aspects of geoprivacy in the context of participatory sensing data. Then, we propose privacy-preserving steps in four categories, namely, ensuring secure and safe settings, actions prior to the start of a research survey, processing and analysis of collected data, and safe disclosure of datasets and research deliverables.

Background: Predictive policing and crime analytics with a spatiotemporal focus get increasing attention among a variety of scientific communities and are already being implemented as effective policing tools. The goal of this paper is to provide an overview and evaluation of the state of the art in spatial crime forecasting focusing on study design and technical aspects. Methods: We follow the PRISMA guidelines for reporting this systematic literature review and we analyse 32 papers from 2000 to 2018 that were selected from 786 papers that entered the screening phase and a total of 193 papers that went through the eligibility phase. The eligibility phase included several criteria that were grouped into: (a) the publication type, (b) relevance to research scope, and (c) study characteristics. Results: The most predominant type of forecasting inference is the hotspots (i.e. binary classification) method. Traditional machine learning methods were mostly used, but also kernel density estimation based approaches, and less frequently point process and deep learning approaches. The top measures of evaluation performance are the Prediction Accuracy, followed by the Prediction Accuracy Index, and the F1-Score. Finally, the most common validation approach was the train-test split while other approaches include the cross-validation, the leave one out, and the rolling horizon. Limitations: Current studies often lack a clear reporting of study experiments, feature engineering procedures, and are using inconsistent terminology to address similar problems. Conclusions: There is a remarkable growth in spatial crime forecasting studies as a result of interdisciplinary technical work done by scholars of various backgrounds. These studies address the societal need to understand and combat crime as well as the law enforcement interest in almost real-time prediction. Implications: Although we identified several opportunities and strengths there are also some weaknesses and threats for which we provide suggestions. Future studies should not neglect the juxtaposition of (existing) algorithms, of which the number is constantly increasing (we enlisted 66). To allow comparison and reproducibility of studies we outline the need for a protocol or standardization of spatial forecasting approaches and suggest the reporting of a study's key data items.

Advances in Geographic Information Science (GISc) and the increasing availability of location data have facilitated the dissemination of crime data and the abundance of crime mapping websites. However, data holders acknowledge that when releasing sensitive crime data there is a risk of compromising the victims' privacy. Hence, protection methodologies are primarily applied to the data to ensure that individual privacy is not violated. This article addresses one group of location protection methodologies, namely geographical masks that are applicable for crime data representations. The purpose is to identify which mask is the most appropriate for crime incident visualizations. A global divergence index (GDi) and a local divergence index (LDi) are developed to compare the effects that these masks have on the original crime point pattern. The indices calculate how dissimilar the spatial information of the masked data is from the spatial information of the original data in regards to the information obtained via spatial crime analysis. The results of the analysis show that the variable radius mask and the donut geomask should be primarily used for crime representations as they produce less spatial information divergence of the original crime point pattern than the alternative local random rotation mask and circular mask. BackgroundGeoprivacy is the privacy of our location, for instance where we are right now, where we live, or where we perform our activities. According to Beresford and Stajano (2003) location privacy is "the ability to prevent other parties from learning ones current or past location". Similarly, Duckham and Kulik (2006) describe location privacy, "as a special type of information privacy which concerns the claim of individuals to determine for themselves when, how, and to what extent location information about them is communicated to others". Researchers have explored geoprivacy by mainly investigating two aspects: (1) the private information disclosure when location data are released; and (2) the development of location protection methodologies to avoid information disclosure. The first aspect reveals the problem, whereas the second aspect gives a solution to the problem. Private Information DisclosureWhen location data are released there is a risk of disclosing private information about those involved in the dataset. Social media location applications allow users to share their current

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.