Review of software for space-time disease surveillance

Disease surveillance makes use of information technology at almost every stage of the process, from data collection and collation, through to analysis and dissemination. Automated data collection systems enable near-real time analysis of incoming data. This context places a heavy burden on software used for space-time surveillance. In this paper, we review software programs capable of space-time disease surveillance analysis, and outline some of their salient features, shortcomings, and usability. Programs with space-time methods were selected for inclusion, limiting our review to ClusterSeer, SaTScan, GeoSurveillance and the Surveillance package for R. We structure the review around stages of analysis: preprocessing, analysis, technical issues, and output. Simulated data were used to review each of the software packages. SaTScan was found to be the best equipped package for use in an automated surveillance system. ClusterSeer is more suited to data exploration, and learning about the different methods of statistical surveillance

Crime geo-surveillance in microscale urban environments: NetSurveillance

Events and phenomena such as crime incidents and outbreak of an epidemic tend to form concentration of high risks known as hotspots. Geosurveillance is an increasingly popular notion for detecting and monitoring the emergence of and changes in hotspots. Yet the existing range of methods are not designed to accurately detect emerging risks at the micro-scale of street-address level. This study proposes NetSurveillance, a method for monitoring the emergence of significant concentration of events along the intricate network of urban streets. Through a simulation test, the study demonstrates the high accuracy of NetSurveillance in detecting such clusters, and outperforms its conventional counterpart conclusively when applied at the individual street address level. Empirical analysis of drug incidents from Chicago also illustrates its capacity to identify rapid outburst of crimes as well as a more gradual build-up of such concentration, and their disappearance, either as a one-off or as part of a recurrent hotbed

A bootstrap based space–time surveillance model with an application to crime occurrences

Space-time cluster, Surveillance, Crime analysis, C15,

Emerging Hydro-Climatic Patterns, Teleconnections and Extreme Events in Changing World at Different Timescales

This Special Issue is expected to advance our understanding of these emerging patterns, teleconnections, and extreme events in a changing world for more accurate prediction or projection of their changes especially on different spatial–time scales

Spatiotemporal crime patterns and the urban environment: Evidence for planning and place-based policy

Crime and disorder influence individual quality of life, community social cohesion, and processes of neighbourhood and urban change. Existing studies that analyze local crime and disorder patterns generally focus only on where crime and disorder events occur. However, understanding the spatiotemporal patterning of crime and disorder, or both where and when events occur, is central to the design, implementation, and evaluation of crime prevention policies and programs. This dissertation explores the connections between local spatiotemporal patterns of crime and disorder, the urban environment, and urban planning through three research articles. Each article makes theoretical contributions that improve understanding of how characteristics of the urban environment influence crime and disorder, methodological contributions that advance spatiotemporal modeling of small-area crime data, and policy-oriented contributions that inform place-based crime prevention initiatives in urban planning, local government, and law enforcement. The first research article examines if, and how, physical disorder, social disorder, property crime, and violent crime share a common spatial pattern and/or a common time trend. Three multivariate models are compared and the results of the best-fitting model show that all crime and disorder types share a common spatial pattern and a common time trend. The shared spatial pattern is found to explain the largest proportion of variability for all types of crime and disorder, and type-specific spatiotemporal hotspots of crime and disorder are identified and investigated to contextualize broken windows theory. This study supports collective efficacy theory, which contends that multiple crime and disorder types are associated with same underlying processes, and highlights specific areas where crime prevention interventions should be designed to address all, or only one, type(s) of crime and disorder. The second article quantifies the time-varying relationships between land use and property crime for twelve seasons at the small-area scale. A set of spatiotemporal regression models with time-constant and time-varying regression coefficients are compared and the results of the best-fitting model show that parks and eating and drinking establishments exhibit recurring seasonal relationships, where parks are more positively associated with property crime during spring/summer and eating and drinking establishments are more positively associated with property crime during autumn/winter. Local land use composition is shown to have a more substantial impact on the spatial, rather than the spatiotemporal, patterning of crime. Applied to policy, the results of this article inform the design and coordination of time-constant and time-varying crime prevention initiatives as implemented by urban planning and law enforcement agencies, respectively. The third article investigates the spatiotemporal patterning of violent crime across multiple spatial scales. Violent crime data are measured at the small-area scale (lower-level units) and small-areas are nested in neighbourhoods, electoral wards, and patrol zones (higher-level units). A cross-classified multilevel model is applied to accommodate the three higher-level units that are non-hierarchical and have overlapping boundaries. Accounting for sociodemographic, built environment, and civic engagement characteristics, planning neighborhoods, electoral wards, and patrol zones are found to explain approximately fourteen percent of the total spatiotemporal variation of violent crime. Planning neighborhoods are the most important source of variation amongst the higher-level units. This article advances understanding of the multiscale processes that influence where and when violent crime events occur and provides area-specific crime risk information within the geographical frameworks used by policymakers in urban planning (neighbourhoods), local government (wards), and law enforcement (patrol zones). Broadly, this dissertation advances research focused on the connections between crime and disorder and the urban environment by (1) quantifying the degree to which spatiotemporal crime and disorder patterns are stable and/or dynamic, (2) examining the relationships between crime and disorder and local sociodemographic and built environment characteristics, (3) illustrating a set of statistical models that make sense of spatiotemporal crime and disorder patterns at the small-area scale, and (4) providing local spatiotemporal information that can be used to design and implement place-based crime prevention initiatives in urban planning, local government, and law enforcement