Enhancing the Jaquez k Nearest Neighbor Test for Space-Time Interaction

The Jacquez k nearest neighbor test, originally developed to improve upon shortcomings of existing tests for space-time interaction, has been shown to be a robust and powerful method of detecting interaction. Despite its flexibility and power however, the test has three main shortcomings: (1) it discards important information regarding the spatial and temporal scale at which detected interac- tion takes place; (2) the results of the test have not been visualized; (3) recent research demonstrates the test to be susceptible to population shift bias. This study presents enhancements to the Jacquez k nearest neighbors test with the goal of addressing each of these three shortcomings and improving the utility of the test. Data on Burkitt’s lymphoma cases in Uganda between 1961-1975 are employed to illustrate the modifications and enhance the visual output of the test. Output from the enhanced test is compared to that provided by alternative tests of space-time interaction. Results show the enhancements presented in this study transform the Jacquez test into a complete, descriptive, and informative metric that can be used as a stand alone measure of global space-time interaction.space-time interaction, Jacquez k nearest neighbor, visualization, space-time cube, population shift bias

Dengue Epidemiology In An Urban Slum Community In Salvador, Brazil

Brazil has experienced a major increase in the incidence of dengue fever and severe dengue since the mid-1990s. Due to incomplete vector-control and transmission prevention efforts, all four serotypes of the dengue virus now circulate in Brazil. Communities such as Pau da Lima, an urban slum in the city of Salvador, Brazil, face a high burden of disease. Beginning in 2009 enhanced surveillance for dengue fever and other acute febrile illnesses has been conducted at the São Marcos Emergency Center (CESM), the only public urgent care health center in Pau da Lima. This sentinel surveillance site serves as a model for acute febrile illness surveillance site for Brazil and other dengue-endemic countries. However, dengue epidemiology and patterns of disease transmission within the community are poorly understood. The objective of this study is to assess dengue risk factors by comparing the demographics of laboratory-confirmed dengue patients with the population of patients who presented to CESM with fever within a two-year period, from January 2009 to December 2010. A total of 282 laboratory-confirmed cases were identified. Univariate logistic regression revealed that young age, brown/mixed race, lower income, and fewer self-reported days of illness at the time of presentation were statistically significant predictors of dengue infection. Dengue cases were significantly clustered in space and time, indicating local transmission of dengue within and between households. The presence of these factors serves as an impetus for targeting vector control and other preventive measures in this community and throughout the rest of the city

Unraveling urban form and collision risk: The spatial distribution of traffic accidents in Zanjan, Iran

Official statistics demonstrate the role of traffic accidents in the increasing number of fa-talities, especially in emerging countries. In recent decades, the rate of deaths and injuries caused by traffic accidents in Iran, a rapidly growing economy in the Middle East, has risen significantly with respect to that of neighboring countries. The present study illustrates an exploratory spatial analysis’ framework aimed at identifying and ranking hazardous locations for traffic accidents in Zanjan, one of the most populous and dense cities in Iran. This framework quantifies the spatiotem-poral association among collisions, by comparing the results of different approaches (including Kernel Density Estimation (KDE), Natural Breaks Classification (NBC), and Knox test). Based on descriptive statistics, five distance classes (2–26, 27–57, 58–105, 106–192, and 193–364 meters) were tested when predicting location of the nearest collision within the same temporal unit. The empirical results of our work demonstrate that the largest roads and intersections in Zanjan had a significantly higher frequency of traffic accidents than the other locations. A comparative analysis of distance bandwidths indicates that the first (2–26 m) class concentrated the most intense level of spatiotem-poral association among traffic accidents. Prevention (or reduction) of traffic accidents may benefit from automatic identification and classification of the most risky locations in urban areas. Thanks to the larger availability of open-access datasets reporting the location and characteristics of car accidents in both advanced countries and emerging economies, our study demonstrates the potential of an integrated analysis of the level of spatiotemporal association in traffic collisions over metropolitan regions

Geomatics Applications to Contemporary Social and Environmental Problems in Mexico

Trends in geospatial technologies have led to the development of new powerful analysis and representation techniques that involve processing of massive datasets, some unstructured, some acquired from ubiquitous sources, and some others from remotely located sensors of different kinds, all of which complement the structured information produced on a regular basis by governmental and international agencies. In this chapter, we provide both an extensive revision of such techniques and an insight of the applications of some of these techniques in various study cases in Mexico for various scales of analysis: from regional migration flows of highly qualified people at the country level and the spatio-temporal analysis of unstructured information in geotagged tweets for sentiment assessment, to more local applications of participatory cartography for policy definitions jointly between local authorities and citizens, and an automated method for three dimensional (3D) modelling and visualisation of forest inventorying with laser scanner technology

Identifying a spatial scale for the analysis of residential burglary: An empirical framework based on point pattern analysis

A key issue in the spatial and temporal analysis of residential burglary is the choice of scale: spatial patterns might differ appreciably for different time periods and vary across geographic units of analysis. Based on point pattern analysis of burglary incidents in Columbus, Ohio during a 9-year period, this study develops an empirical framework to identify a useful spatial scale and its dependence on temporal aggregation. Our analysis reveals that residential burglary in Columbus clusters at a characteristic scale of 2.2 km. An ANOVA test shows no significant impact of temporal aggregation on spatial scale of clustering. This study demonstrates the value of point pattern analysis in identifying a scale for the analysis of crime patterns. Furthermore, the characteristic scale of clustering determined using our method has great potential applications: (1) it can reflect the spatial environment of criminogenic processes and thus be used to define the spatial boundary for place-based policing; (2) it can serve as a candidate for the bandwidth (search radius) for hot spot policing; (3) its independence of temporal aggregation implies that police officials need not be concerned about the shifting sizes of risk-areas depending on the time of the year

Spatio-Temporal Analysis of Crime Incidents for Forensic Investigation

Crime analysis and mapping has been routinely employed to gather intelligence which informs security efforts and forensic investigations. Traditionally, geographic information systems in the form of third-party mapping applications are used for analysis of crime data but are often expensive and lack flexibility, transparency, or efficiency in uncovering associations and relationships in crime. Each crime incident and article of evidence within that incident has an associated spatial and temporal component which may yield significant and relevant information to the case. Wide variations exist in the techniques that departments use and commonly spatial and temporal components of crime are evaluated independently, if at all. Thus, there is a critical need to develop and implement spatio-temporal investigative strategies so police agencies can gain a foundational understanding of crime occurrence within their jurisdiction, develop strategic action for disruption and resolution of crime, conduct more informed investigations, better utilize resources, and provide an overall more effective service. The purpose of this project was to provide foundational knowledge to the investigative and security communities and demonstrate the utility of empirical spatio-temporal methods for the assessment and interpretation of crime incidents. Two software packages were developed as an open source (R) solution to expand current techniques and provide an implementable spatio-temporal methodology for crime analysis. Additionally, an actionable method for near repeat analysis was developed. Firstly, the premise of the near repeat phenomenon was evaluated across crime types and cities to discern optimal parameters for spatial and temporal bandwidths. Using these parameters, a method for identifying near repeat series was developed which draws inter-incident linkages given the spatio-temporal clustering of the incidents. Resultant crime networks and maps provide insight regarding near repeat crime incidents within the landscape of their jurisdiction for targeted investigation. Finally, a new approach to the geographic profiling problem was developed which assesses and integrates the travel environment of road networks, beliefs and assumptions formed through the course of the investigation process about the perpetrator, and information derived from the analysis of evidence. Each piece of information is evaluated in conjunction with spatio-temporal routing functions and then used to update prior beliefs about the anchor point of the perpetrator. Adopting spatio-temporal methodologies for the investigation of crime offers a new framework for forensic operations in the investigation of crime. Systematic consideration about the value and implications of the relationship between space, time, and crime was shown to provide insight regarding crime. In a forward-looking sense this work shows that the interpretation of crime within a spatio-temporal context can provide insight into crime occurrence, linkage of crime incidents, and investigations of those incidents

Spatio-temporal methods for the analysis of crime and traffic safety data

Desde que John Snow analizara espacialmente los casos de cólera de la epidemia de Londres de 1854, han sido muchas las disciplinas que se han beneficiado de la existencia de métodos estadísticos espacio-temporales: agricultura, astronomía, biología, epidemiología, geología, hidrología, meteorología y teledetección, entre otras. Esta tesis se centra en el desarrollo y aplicación de estos métodos en el contexto de dos disciplinas: la seguridad vial y la criminología. En particular, un objetivo capital ha sido el de detectar lagunas de investigación en la literatura actualmente disponible. Así pues, la investigación de diversos problemas que surgen de forma habitual en estas dos áreas, los cuales requieren de un tratamiento estadístico concreto, ha llevado a estructurar la tesis de la forma siguiente. En primer lugar, tras un capítulo introductorio, se exponen dos estudios sobre seguridad vial sobre una estructura de tipo red. Así pues, el Capítulo 2 contiene un análisis multivariante a nivel de calle en el que se distingue entre zonas de intersección y de no intersección. Seguidamente, en el Capítulo 3 se presenta un método para la detección de “hotspots” de riesgo diferencial sobre una red. El Capítulo 4 incluye un análisis espacio-temporal de un conjunto de datos de robos a vivienda centrado en el fenómeno de casi-repetición, el cual es capital en criminología. La versión clásica del test de Knox es adaptada para contemplar la existencia de heterogeneidad espacio-temporal en el riesgo de robo, lo que permite obtener una visión más precisa de la magnitud del fenómeno. En concreto, se propone un ajuste adecuado en un contexto de ausencia de variación espacio-temporal tanto en la variable exposición como en las covariables. El Capítulo 5 incluye un estudio detallado del problema de la unidad de área modificable (MAUP) en el contexto del análisis de la seguridad vial. Como novedad frente a estudios previos, la escala y la zonificación de las estructuras espaciales son controladas de forma explícita. Además, el análisis no solo se centra en las consecuencias finales en términos de estimación y precisión de los modelos, sino en las alteraciones que sufren las variables. El Capítulo 6 se dedica a la comparación de varias metodologías que permiten analizar cómo la proximidad a ciertos lugares influye en la incidencia de un evento de interés. En concreto, esta comparación se realiza para valorar la relación existente entre los accidentes de tráfico y la localización de centros educativos. El Capítulo 7 se centra en analizar una cuestión a la que se ha dado gran importancia en criminología cuantitativa: la pérdida de fiabilidad de un análisis como consecuencia de la presencia de eventos no geocodificados. Se ha estimado que alcanzar un 85% en la tasa de geocodificación es lo suficientemente aceptable como para analizar los datos. En esta tesis se reestima este porcentaje teniendo en cuenta algunos factores y métodos no tenidos en cuenta en la estimación inicial. Se concluye que geocodificar el 85% de los eventos puede no ser suficiente bajo ciertas condiciones. Finalmente, el Capítulo 8 incluye la descripción de dos paquetes de R que han sido desarrollados durante esta tesis: SpNetPrep, que permite el preprocesado y depuración de una estructura de tipo red, y DRHotNet, que implementa el procedimiento de detección de “hotspots” descrito en el Capítulo 3.Since physician John Snow analyzed the spatial distribution of cholera cases detected in the 1854 epidemic in London, many disciplines have benefited from the existence of spatio-temporal statistical methods: agriculture, astronomy, biology, epidemiology, geology, hydrology, meteorology, and remote sensing, among others. This thesis therefore focuses on the development and application of spatio-temporal methods in the context of two disciplines: traffic safety analysis and criminology. In particular, a capital objective has been to detect research gaps in the currently available literature. Thus, the investigation of several types of problems that usually arise in these two fields, which require a specific statistical approach, has led to the structuring of this thesis as follows. Firstly, after an introductory chapter, two studies in the context of traffic safety analysis where the use of a linear network structure is fundamental are shown. The first one contains a street-level multivariate analysis of the occurrence of traffic accidents accounting for the presence of intersection and non-intersection segments. Next, in Chapter 3, a method is presented and employed for the detection of differential risk "hotspots" along a network. Chapter 4 includes a spatio-temporal analysis of a burglary dataset focused on the phenomenon of near-repetition, which is capital in the field of criminology. The classic version of the Knox test is adapted to account for spatio-temporal burglary risk heterogeneity, which provides a more accurate representation of the magnitude of the phenomenon. Specifically, an adjustment is proposed that is suitable in a context of absence of spatial-temporal variation in both the exposure variable and the covariates. Chapter 5 includes a detailed study of the modifiable area unit problem (MAUP) in the context of traffic safety analysis. As a novelty compared to previous studies, the scale and zoning of the spatial structures considered are explicitly controlled. Furthermore, the analysis does not only focus on the final consequences in terms of estimation and precision of the models, but also on the alterations that occur in the different variables involved. Chapter 6 is dedicated to the comparison of several methodologies that can be selected to analyze how the proximity to certain places influences the incidence of an event of interest. Specifically, this comparison is made to assess the relationship between traffic accidents and the location of educational centers. Chapter 7 focuses on analyzing an issue that has been given great importance in quantitative criminology: the loss of reliability of analyses as a result of the presence of non-geocoded events. It has been estimated that reaching 85% geocoding success rate is enough to carry out further analysis of the data. In this thesis, this percentage is reestimated taking into account some factors and methods not taken into account in the initial estimation. It is concluded that reaching 85% success rate in the geocoding process may not be sufficient under certain conditions. Finally, Chapter 8 includes the description of two R packages that have been developed during this thesis: SpNetPrep, which allows the preprocessing and curation of a linear network, and DRHotNet, which implements the "hotspot" detection procedure described in Chapter 3

Understanding the deterrent effect of police patrol

The fact that crime clusters spatially has been known since at least the early 19th century. However, understanding of the extent and nature of this clustering at different areal units, and the fact that crime also clusters at different temporal scales is relatively new. Where previously the most at-risk areas (or `hot-spots') of crime were defined over areas the size of city districts and for periods of months if not years, the last decade has seen the focus shift to micro-places - areas of only a few hundred metres across - which are only `hot' for days or even hours. The notion that visible police presence in crime hot-spots can deter crime is not new and has been the basis of police patrols for two centuries. This deterrent effect has been well evidenced in many previous studies, both by academics and police practitioners. However, evaluations of these more recent micro-level hot-spot patrol strategies face significant analytic challenges and data quality concerns. They also often assume levels of police activity at the micro-area level (an `intention-to-treat' design) rather than measuring it directly. The aim of this thesis is to investigate the accuracy and precision of data that can be used to evaluate micro-level hot-spot patrol strategies and the implications this has for any analysis conducted using such data at these micro-level geographies. This thesis begins by outlining the relevant literature regarding place-based policing strategies and the current understanding of how crime clusters in both space and time. It continues by highlighting the data challenges associated with evaluating micro-level police interventions through the use of an illustrative analytic strategy before using a self-exciting point process model to evaluate the effects of police foot patrol in micro-level hot-spot under the assumption that the crime and patrol data being used are accurate. This is followed by two chapters which investigate the quality of the two datasets. Finally, the point-process evaluation is re-conducted using simulated data that takes account of the uncertainty of the datasets to demonstrate how data quality issues effect the result of such an evaluation and ultimately, the perceived efficacy of these highly-focussed policing strategies

Spatio-temporal modelling of civil violence: Four frameworks for obtaining policy-relevant insights

Mathematical modelling of civil violence can be accomplished in different ways. In this thesis, four modelling frameworks are investigated, each of which leads to different insights into the spatio-temporal properties of civil violence. These frameworks vary with respect to the extent in which empirical data is used in generating model assumptions, and the extent in which simplifying assumptions distance the model from the real world. An overarching objective is to compare the insights and underlying assumptions of each framework, and to consider how they might be consolidated to aid policy decision-making. Within each framework, novel contributions both to the mathematical modelling of social systems, and to the theoretical understanding of civil violence are made. First, a novel data-driven approach for analysing local patterns of geographic diffusion in event data is presented, and applied to offences associated with the 2011 London riots. Second, by considering the decision-making of individuals, thereby taking an agent-based perspective, and using existing theory to construct model assumptions, a parametric statistical model of discrete choice is derived that more closely inspects the targets chosen by rioters, and how these choices might have changed over time. The application of this model to the policy domain is explored by considering police deployment strategies. Third, focusing on the interaction between two adversaries, and employing stochastic point process models, a series of multivariate and nonlinear Hawkes processes are proposed and used to explore spatio-temporal dependency during the Naxal insurgency in India. Fourth, a novel spatially-explicit differential equation-based model of conflict escalation between two adversaries is derived. A bifurcation is identified that results from the spatial disaggregation of the model. Implications for the interpretation of the model in the real world and potential applications are discussed

Current practices in the spatial analysis of cancer: flies in the ointment

While many lessons have been learned from the spatial analysis of cancer, there are several caveats that apply to many, if not all such analyses. As "flies in the ointment", these can substantially detract from a spatial analysis, and if not accounted for, can lead to weakened and erroneous conclusions. This paper discusses several assumptions and limitations of spatial analysis, identifies problems of scientific inference, and concludes with potential solutions and future directions