encephalitis in Florida

Background: Eastern Equine Encephalitis virus (EEEV) is an alphavirus with high pathogenicity in both humans and horses. Florida continues to have the highest occurrence of human cases in the USA, with four fatalities recorded in 2010. Unlike other states, Florida supports year-round EEEV transmission. This research uses GIS to examine spatial patterns of documented horse cases during 2005–2010 in order to understand the relationships between habitat and transmission intensity of EEEV in Florida. Methods: Cumulative incidence rates of EEE in horses were calculated for each county. Two cluster analyses were performed using density-based spatial clustering of applications with noise (DBSCAN). The first analysis was based on regional clustering while the second focused on local clustering. Ecological associations of EEEV were examined using compositional analysis and Euclidean distance analysis to determine if the proportion or proximity of certain habitats played a role in transmission. Results: The DBSCAN algorithm identified five distinct regional spatial clusters that contained 360 of the 438 horse cases. The local clustering resulted in 18 separate clusters containing 105 of the 438 cases. Both the compositional analysis and Euclidean distance analysis indicated that the top five habitats positively associated with horse cases were rural residential areas, crop and pastureland, upland hardwood forests, vegetated non-forested wetlands, an

Time-Geographic Density Estimation for Moving Point Objects

This research presents a time-geographic method of density estimation for moving point objects. The approach integrates traditional kernel density estimation (KDE) with techniques of time geography to generate a continuous intensity surface that characterises the spatial distribution of a moving object over a fixed time frame. This task is accomplished by computing density estimates as a function of a geo-ellipse generated for each consecutive pair of control points in the object’s space-time path and summing those values at each location in a manner similar to KDE. The main advantages of this approach are: (1) that positive intensities are only assigned to locations within a moving object’s potential path area and (2) that it avoids arbitrary parameter selection as the amount of smoothing is controlled by the object’s maximum potential velocity. The time-geographic density estimation technique is illustrated with a sample dataset, and a discussion of limitations and future work is provided

Mapping Sex Offender Activity Spaces Relative to Crime Using Time-Geographic Methods

Activity spaces describe the physical area typically encountered by an individual person during his or her routine daily activities. Activity spaces encompass a person’s anchor points – locations that are frequented regularly, such as home, work, school, and recreational areas – and the travel paths that connect them. Activity spaces of criminals are routinely mapped in order to better understand the spatial patterns and processes of crime at the individual level. Although many geographic information system-based methods have been used to map activity spaces over the years, potential path areas are becoming a preferred method since they incorporate both spatial and temporal data, as well as time budget and mobility constraints. This paper extends potential path areas for mapping activity spaces of criminals in two ways. First, time-geographic density estimation (TGDE) is used to estimate individual activity spaces using potential path areas that have associated probability densities. Second, activity spaces of numerous individuals are combined into a single intensity surface that maps areas of a city that are more frequented by offenders and, accordingly, expected to support higher crime rates. The approach is demonstrated using a dataset of home and work addresses of registered sex offenders in the city of St. Louis. The final density surface of their combined activity spaces is related to the locations of reported sex crimes. The results highlight how sex crimes are concentrated in offender activity spaces and suggest the approach might be useful for predictive policing

Calling: Earth #007 - Joni Downs Firat, Wildlife Ecologist

Joni Downs Firat, the Associate Chair of the USF School of Geosciences, discusses her research into wildlife ecology and her interest in supporting teaching and research for graduate and undergraduate students. More about Joni can be found here: http://hennarot.forest.usf.edu/main/depts/geosci/faculty/jdowns

Integrating People and Place: a Density-Based Measure for Assessing Accessibility to Opportunities

Mobile object analysis is a well-studied area of transportation and geographic information science (GIScience). Mobile objects may include people, animals, or vehicles. Time geography remains a key theoretical framework for understanding mobile objects\u27 movement possibilities. Recent efforts have sought to develop probabilistic methods of time geography by exploring questions of data uncertainty, spatial representation, and other limitations of classical approaches. Along these lines, work has blended time geography and kernel density estimation in order to delineate the probable locations of mobile objects in both continuous and discrete network space. This suite of techniques is known as time geographic density estimation (TGDE). The present paper explores a new direction for TGDE, namely the creation of a density-based accessibility measure for assessing mobile objects\u27 potential for interacting with opportunity locations. As accessibility measures have also garnered widespread attention in the literature, the goal here is to understand the magnitude and nature of the opportunities a mobile object had access to, given known location points and a time budget for its movement. New accessibility measures are formulated and demonstrated with synthetic trip diary data. The implications of the new measures are discussed in the context of people-based vs. placed-based accessibility analyses

Measuring and Visualizing Place-Based Space-Time Job Accessibility

Place-based accessibility measures, such as the gravity-based model, are widely applied to study the spatial accessibility of workers to job opportunities in cities. However, gravity-based measures often suffer from three main limitations: (1) they are sensitive to the spatial configuration and scale of the units of analysis, which are not specifically designed for capturing job accessibility patterns and are often too coarse; (2) they omit the temporal dynamics of job opportunities and workers in the calculation, instead assuming that they remain stable over time; and (3) they do not lend themselves to dynamic geovisualization techniques. In this paper, a new methodological framework for measuring and visualizing place-based job accessibility in space and time is presented that overcomes these three limitations. First, discretization and dasymetric mapping approaches are used to disaggregate counts of jobs and workers over specific time intervals to a fine-scale grid. Second, Shen\u27s (1998) gravity-based accessibility measure is modified to account for temporal fluctuations in the spatial distributions of the supply of jobs and the demand of workers and is used to estimate hourly job accessibility at each cell. Third, a four-dimensional volumetric rendering approach is employed to integrate the hourly job access estimates into a space-time cube environment, which enables the users to interactively visualize the space-time job accessibility patterns. The integrated framework is demonstrated in the context of a case study of the Tampa Bay region of Florida. The findings demonstrate the value of the proposed methodology in job accessibility analysis and the policy-making process

Network-Based Home Range Analysis Using Delaunay Triangulation

The home range is the fundamental measurement of fish and wildlife space-use patterns. Kernel density estimation (KDE) is the most widely applied home range estimator, although its poor performance has recently been documented. In this paper, we suggest that KDE is inappropriate for home range estimation, because it assumes Euclidean-based space usage. Because animal space-use patterns show characteristics of network-based movement, we develop a network-based home range estimator. First, we use Delaunay triangulation (DT) to approximate a network of travel paths from a set of animal point locations. Then, we adapt KDE to estimate home ranges as a function of that network. Preliminary results suggest that network-based home range estimation using DT has the potential to improve the way ecologists measure animal space-use patterns

Integrating People and Place: a Density-Based Measure for Assessing Accessibility to Opportunities

Mobile object analysis is a well-studied area of transportation and geographic information science (GIScience). Mobile objects may include people, animals, or vehicles. Time geography remains a key theoretical framework for understanding mobile objects\u27 movement possibilities. Recent efforts have sought to develop probabilistic methods of time geography by exploring questions of data uncertainty, spatial representation, and other limitations of classical approaches. Along these lines, work has blended time geography and kernel density estimation in order to delineate the probable locations of mobile objects in both continuous and discrete network space. This suite of techniques is known as time geographic density estimation (TGDE). The present paper explores a new direction for TGDE, namely the creation of a density-based accessibility measure for assessing mobile objects\u27 potential for interacting with opportunity locations. As accessibility measures have also garnered widespread attention in the literature, the goal here is to understand the magnitude and nature of the opportunities a mobile object had access to, given known location points and a time budget for its movement. New accessibility measures are formulated and demonstrated with synthetic trip diary data. The implications of the new measures are discussed in the context of people-based vs. placed-based accessibility analyses

A Wildlife Movement Approach to Optimally Locate Wildlife Crossing Structures

Transportation networks negatively impact wildlife populations by limiting the physical movement of the individual animal. In extreme cases road presence can lead to collisions between vehicles and animals, resulting in direct mortality if an animal attempts to cross the road. Crossing structures are one commonly used method for reducing wildlife–vehicle collisions. However, limited funding often reduces the amount of structures that may be constructed in practice. Therefore, areas that have the highest probability for animal interactions with roads should be targeted for locating new structures to provide the best possible outcome. This research uses a probabilistic time-geographic strategy coupled with a site selection phase handled by a classical optimization model to site wildlife crossing structures. To achieve optimal site selection, crossing locations are first identified where wildlife frequently cross roads, and then a maximum covering location problem is applied to these areas as demand nodes. The objective is to cover the largest area having the highest probability of interaction given a finite number of crossing structures available to be located. Coverage is defined in terms of fencing distance associated with a particular structure. The approach was demonstrated using Florida panther telemetry data identifying potential crossing structures across two counties in south Florida. The maximal covering location problem (MCLP) was solved for four coverage distances using radio telemetry tracking data, which captured frequent contact with roads. The results identify that the most effective coverage distance is 2000 m, which incrementally covers more total animal–road interaction probability than that of lower fencing distances in the case of the Florida panther. The results illustrate how this new time-geographic approach, combined with location modeling, measures animal–road interactions probabilistically for finding the optimum placement of wildlife crossing structures