Joni A. Downs

Effects of Point Pattern Shape on Home-Range Estimates

Abstract Home-range estimators are commonly tested with simulated animal locational data in the laboratory before the estimators are used in practice. Although kernel density estimation (KDE) has performed well as a home-range estimator for simulated data, several recent studies have reported its poor performance when used with data collected in the field. This difference may be because KDE and other home-range estimators are generally tested with simulated point locations that follow known statistical distributions, such as bivariate normal mixtures, which may not represent well the space-use patterns of all wildlife species. We used simulated animal locational data of 5 point pattern shapes that represent a range of wildlife utilization distributions to test 4 methods of home-range estimation: 1) KDE with reference bandwidths, 2) KDE with least-squares cross-validation, 3) KDE with plug-in bandwidths, and 4) minimum convex polygon (MCP). For the point patterns we simulated, MCP tended to produce more accurate area estimates than KDE methods. However, MCP estimates were markedly unstable, with bias varying widely with both sample size and point pattern shape. The KDE methods performed best for concave distributions, which are similar to bivariate normal mixtures, but still overestimated home ranges by about 40–50% even in the best cases. For convex, linear, perforated, and disjoint point patterns, KDE methods overestimated home-range sizes by 50–300%, depending on sample size and method of bandwidth selection. These results indicate that KDE does not produce home-range estimates that are as accurate as the literature suggests, and we recommend exploring other techniques of home-range estimation.

Optimizing hurricane disaster relief goods distribution: model development and application with respect to planning strategies.

Over the last few years, hurricane emergencies have been among the most pervasive major disruptions in the United States, particularly in the south-east region of the country. A key aspect of managing hurricane disasters involves logistical planning to facilitate the distribution and transportation of relief goods to populations in need. This study shows how a variant of the capacitated warehouse location model can be used to manage the flow of goods shipments to people in need. In this application, the model is used with protocols set forth in Floridas Comprehensive Emergency Plan and tested in a smaller city in north Florida. Scenarios explore the effects of alternate goods distribution strategies on the provision of disaster relief. Results show that measures describing peoples accessibility to relief goods are affected by the distribution infrastructure used to provide relief, as well as assumptions made regarding the population(s) assumed to be in need of aid.

A Characteristic-Hull Based Method for Home Range Estimation

Recent literature has reported inaccuracies associated with some popular home range estimators such as kernel density estimation, especially when applied to point patterns of complex shapes. This study explores the use of characteristic hull polygons (CHPs) as a new method of home range estimation. CHPs are special bounding polygons created in GIS that can have concave edges, be composed of disjoint regions, and contain areas of unoccupied space within their interiors. CHPs are created by constructing the Delaunay triangulation of a set of points and then removing a subset of the resulting triangles. Here, CHPs consisting of 95% of the smallest triangles, measured in terms of perimeter, are applied for home range estimation. First, CHPs are applied to simulated animal locational data conforming to five point pattern shapes at three sample sizes. Then, the method is applied to black-footed albatross (Phoebastria nigripes) locational data for illustration and comparison to other methods. For the simulated data, 95% CHPs produced unbiased home range estimates in terms of size for linear and disjoint point patterns and slight underestimates (8–20%) for perforated, concave, and convex ones. The estimated and known home ranges intersected one another by 72–96%, depending on shape and sample size, suggesting that the method has potential as a home range estimator. Additionally, the CHPs applied to estimate albatross home ranges illustrate how the method produces reasonable estimates for bird species that intensively forage in disjoint habitat patches.

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 objects 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 objects potential path area and (2) that it avoids arbitrary parameter selection as the amount of smoothing is controlled by the objects 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.

Time-Geographic Density Estimation for Home Range Analysis

This research presents time-geographic density estimation (TGDE) as a new technique of animal home range analysis in geographic information science (GIS). TGDE combines methodologies of time geography and statistical density estimation to produce a continuous probability distribution of an objects spatial position over time. Once TGDE is applied to animal tracking data to create a density surface, home ranges and core areas can be delineated using specified contours of relative intensity (e.g., 95% or 50%). This article explores the use of TGDE for home range analysis using three data sets: a fixed-interval simulated data set and two variable-interval satellite tracks for a loggerhead sea turtle (Caretta caretta) corresponding to internesting and post-migration foraging periods. These applications are used to illustrate the influence of several parameters, including sample size, temporal sampling scheme, selected distance-weighted geoellipse function, and specified maximum velocity, on home range estimates. The results demonstrate how TGDE produces reasonable home range estimates even given irregular tracking data with wide temporal gaps. The advantages of TGDE as compared with traditional methods of home range estimation such as kernel density estimation are as follows: (1) intensities are not assigned to locations where the animal could not have been located given space and time constraints; (2) the density surface represents the actual uncertainty about an animals spatial position during unsampled time periods; (3) the amount of smoothing applied is objectively specified based on the animals movement velocity rather than arbitrarily chosen; and (4) uneven sampling intervals are easily accommodated since the density estimates are calculated based on the elapsed time between observed locations. In summary, TGDE is a useful method of home range estimation and shows promise for numerous applications to moving objects in GIS.

The Geography of Conflict and Death in Belfast, Northern Ireland

The conflict known as the “Troubles” in Northern Ireland began during the late 1960s and is defined by political and ethno-sectarian violence between state, pro-state, and anti-state forces. Reasons for the conflict are contested and complicated by social, religious, political, and cultural disputes, with much of the debate concerning the victims of violence hardened by competing propaganda-conditioning perspectives. This article introduces a database holding information on the location of individual fatalities connected with the contemporary Irish conflict. For each victim, it includes a demographic profile, home address, manner of death, and the organization responsible. Employing geographic information system (GIS) techniques, the database is used to measure, map, and analyze the spatial distribution of conflict-related deaths between 1966 and 2007 across Belfast, the capital city of Northern Ireland, with respect to levels of segregation, social and economic deprivation, and interfacing. The GIS analysis includes a kernel density estimator designed to generate smooth intensity surfaces of the conflict-related deaths by both incident and home locations. Neighborhoods with high-intensity surfaces of deaths were those with the highest levels of segregation (> 90 percent Catholic or Protestant) and deprivation, and they were located near physical barriers, the so-called peacelines, between predominantly Catholic and predominantly Protestant communities. Finally, despite the onset of peace and the formation of a power-sharing and devolved administration (the Northern Ireland Assembly), disagreements remain over the responsibility and “commemoration” of victims, sentiments that still uphold division and atavistic attitudes between spatially divided Catholic and Protestant populations.

Voxel-based probabilistic space-time prisms for analysing animal movements and habitat use

Time-geographic analysis has been limited in the past by its capacity to model only potential locations for moving objects, without sufficiently evaluating which locations are more probable. This paper expands upon existing research in probabilistic time geography by accomplishing two main tasks. First, a new geocomputational approach is presented for generating probabilistic space-time prisms. Here, probabilistic space-time prisms are represented as three-dimensional rasters of volume elements, or voxels, that record the probability that an object was located at any location at any time. After describing the geocomputational approach, its utility is illustrated through a detailed analysis of tracking data collected for a Muscovy duck (Cairina mochata). Specifically, probabilistic space-time prisms are used to map the duck’s fine-scale movement patterns over five complete days of global positioning system (GPS)-tracking. Then, the space-time prisms are used in conjunction with a detailed habitat map of the study area in order to quantify the duck’s habitat usage over the course of each day. This application highlights the utility of probabilistic space-time prisms for understanding the movements and activities of animals at fine temporal and spatial scales.

Where were you? Development of a time-geographic approach for activity destination re-construction

Abstract With the use of individual-level travel survey datasets describing the detailed activities of households, it is possible to analyze human movements with a high degree of precision. However, travel survey data are not without quality issues. Potential exists for origins and destinations of reported trips not to be geo-referenced, perhaps due to misreported information or inconsistencies in spatial address databases, which can limit the usefulness of the survey data. From an analytical standpoint, this is a serious problem because a single unreferenced stop in a trip record in effect renders that individual’s data useless, especially in cases where analyzing chains of activity locations is of interest. This paper presents a framework and basic computational approach for exploring unlocatable activity locations inherent to travel surveys. Derived from recent work in developing a network-based, probabilistic time geography, the proposed methods are able to estimate the likely locations of missing trip origins and destinations. The methods generate probabilistic potential path trees which are used to visualize and quantify potential locations for the unreferenced destinations. The methods are demonstrated with simulated survey data from a smaller metropolitan area.

Strategically Locating Wildlife Crossing Structures for Florida Panthers Using Maximal Covering Approaches

Crossing structures are an effective method for mitigating habitat fragmentation and reducing wildlife-vehicle collisions, although high construction costs limit the number that can be implemented in practice. Therefore, optimizing the placement of crossing structures in road networks is suggested as a strategic conservation planning method. This research explores two approaches for using the maximal covering location problem (MCLP) to determine optimal sites to install new wildlife crossing structures. The first approach is based on records of traffic mortality, while the second uses animal tracking data for the species of interest. The objective of the first is to cover the maximum number of collision sites, given a specified number of proposed structures to build, while the second covers as many animal tracking locations as possible under a similar scenario. These two approaches were used to locate potential wildlife crossing structures for endangered Florida panthers (Puma concolor coryi) in Collier, Lee, and Hendry Counties, Florida, a population whose survival is threatened by excessive traffic mortality. Historical traffic mortality records and an extensive radio-tracking dataset were used in the analyses. Although the two approaches largely select different sites for crossing structures, both models highlight key locations in the landscape where these structures can remedy traffic mortality and habitat fragmentation. These applications demonstrate how the MCLP can serve as a useful conservation planning tool when traffic mortality or animal tracking data are available to researchers.

Analysing Infrequently Sampled Animal Tracking Data by Incorporating Generalized Movement Trajectories with Kernel Density Estimation

Abstract When analysing the movements of an animal, a common task is to generate a continuous probability density surface that characterises the spatial distribution of its locations, termed a home range. Traditional kernel density estimation (KDE), the Brownian Bridges kernel method, and time-geographic density estimation are all commonly used for this purpose, although their applicability in some practical situations is limited. Other studies have argued that KDE is inappropriate analysing moving objects, while the latter two methods are only suitable for tracking data collected at frequent enough intervals such that an object’s movement pattern can be adequately represented using a space–time path created by connecting consecutive points. This research formulates and evaluates KDE using generalised movement trajectories approximated by Delaunay triangulation (KDE-DT) as a method for analysing infrequently sampled animal tracking data. In this approach, a DT is constructed from a point pattern of tracking data in order to approximate the network of movement trajectories for an animal. This network represents the generalised movement patterns of an animal rather than its specific, individual trajectories between locations. Then, kernel density estimates are calculated with distances measured using that network. First, this paper describes the method and then applies it to generate a probability density surface for a Florida panther from radio-tracking data collected three times per week. Second, the performance of the technique is evaluated in the context of delineating wildlife home ranges and core areas from simulated animal locational data. The results of the simulations suggest that KDE-DT produces more accurate home range estimates than traditional KDE, which was evaluated with the same data in a previous study. In addition to animal home range analysis, the technique may be useful for characterising a variety of spatial point patterns generated by objects that move through continuous space, such as pedestrians or ships.