Day: September 26, 2011

Time-geographic Density Estimation for Home Range Analysis

/ Matt Artz

Annals of GISAnnals of GIS, Volume 17, Issue 3, 2011

Joni A. Downs, Mark W. Horner, and Anton D. Tucker

“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 object’s 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 animal’s spatial position during unsampled time periods; (3) the amount of smoothing applied is objectively specified based on the animal’s 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.”

On the Spatio-temporal Analysis of Hydrological Droughts from Global Hydrological Models

/ Matt Artz

Hydrology and Earth System SciencesHydrology and Earth System Sciences, 15, 2963-2978, 2011

G. A. Corzo Perez, M. H. J. van Huijgevoort, F. Voß, and H. A. J. van Lanen

“The recent concerns for world-wide extreme events related to climate change have motivated the development of large scale models that simulate the global water cycle. In this context, analysis of hydrological extremes is important and requires the adaptation of identification methods used for river basin models. This paper presents two methodologies that extend the tools to analyze spatio-temporal drought development and characteristics using large scale gridded time series of hydrometeorological data. The methodologies are classified as non-contiguous and contiguous drought area analyses (i.e. NCDA and CDA). The NCDA presents time series of percentages of areas in drought at the global scale and for pre-defined regions of known hydroclimatology. The CDA is introduced as a complementary method that generates information on the spatial coherence of drought events at the global scale.

Results of the CDA method applied for the analysis of number of drought clusters. (a) Drought clusters for 10 January 1976 (around 800 colors, each color represent a cluster and (b) color-coded table of the number of drought clusters on the whole Earth.

Results of the CDA method applied for the analysis of number of drought clusters. (a) Drought clusters for 10 January 1976 (around 800 colors, each color represent a cluster and (b) color-coded table of the number of drought clusters on the whole Earth.

“Spatial drought events are found through CDA by clustering patterns (contiguous areas). In this study the global hydrological model WaterGAP was used to illustrate the methodology development. Global gridded time series of subsurface runoff (resolution 0.5°) simulated with the WaterGAP model from land points were used. The NCDA and CDA were developed to identify drought events in runoff. The percentages of area in drought calculated with both methods show complementary information on the spatial and temporal events for the last decades of the 20th century. The NCDA provides relevant information on the average number of droughts, duration and severity (deficit volume) for pre-defined regions (globe, 2 selected hydroclimatic regions). Additionally, the CDA provides information on the number of spatially linked areas in drought, maximum spatial event and their geographic location on the globe. Some results capture the overall spatio-temporal drought extremes over the last decades of the 20th century. Events like the El Niño Southern Oscillation (ENSO) in South America and the pan-European drought in 1976 appeared clearly in both analyses. The methodologies introduced provide an important basis for the global characterization of droughts, model inter-comparison of drought identified from global hydrological models and spatial event analyses.”

Using Spatial Analysis to Improve Health Care Services and Delivery at Baystate Health

/ Matt Artz

Journal of Map & Geography LibrariesJournal of Map & Geography Libraries, Volume 7, Issue 3, 2011

Jane L. Garb and Richard B. Wait

“Baystate Health has been recognized by the industry for its innovative and wide-ranging use of geographic information systems (GISs) to address problems in health. Spatial analysis is a key component in the effective use of GISs in health care for data exploration, hypothesis testing, and modeling. This paper describes GIS applications by the Health Geographics Program at Baystate Health.

Flow of youth violence and police sectors in Springfield, MA. Arrow width indicates volume of flow.

Flow of youth violence and police sectors in Springfield, MA. Arrow width indicates volume of flow.

“These applications include direct patient care, epidemiologic research, disease prevention and intervention, strategic planning, and marketing. We directed particular attention to the spatial analytic methods used in these applications. Finally, the challenges faced in obtaining and using health data for our analysis are discussed.”