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We used the model output to explore relationships between EEEV occurrence and environmental factors, as well as generated risk maps for each season. We also introduced a normalized difference risk index to identify locations with high risk during both seasons. LULC variables were better predictors of EEEV than bioclimatic or topographic variables. Summer cases occurred in locations that were in or near rural lands at high elevations that were near woody wetlands and coniferous forests, while winter cases occurred primarily in areas with large proportions of evergreen forests and rural land. Because EEEV is often fatal to both horses and humans, the goal was to provide more accurate EEEV risk maps for each season to guide disease management and vector control efforts.

ABSTRACTEastern Equine Encephalitis virus (EEEV) is a virus found predominantly east of the Mississippi River in the United States that can be fatal to both equines and humans. The disease has previously been most prolific in states like Florida, but there has been an increase in the prevalence in other states further up north on the east coast of the United States in recent years. The purpose of this research is to use the ecological niche modelling program Maxent to model EEEV habitat suitability probability. This research utilized data of fatality incidence in equine hosts, versus sentinel chicken infection data, the spatial data traditionally utilized for mapping EEEV. This research produced a map of habitat suitability, which expanded on previous risk models by utilizing additional environmental factors. It confirmed areas of higher probability identified by previous models but identified more narrow areas of higher probability as well. This model adds to the literature applying ecological modelling techniques to spatial epidemiology. It highlights spaces that represent the culmination of environmental factors for the transmission of EEEV. Considering these environmental factors identified can assist in identifying places where there is a higher risk of EEEV as new cases begin to appear.

Known as the “lung of the planet”, the Amazon rainforest produces more than 20% of the Earth’s oxygen. Once a carbon pool for mitigating climate change, the Brazilian Amazônia Biome recently has become a significant carbon emitter due to increasingly frequent wildfires. Therefore, it is of crucial importance for authorities to understand wildfire dynamics to manage them safely and effectively. This study incorporated remote sensing and spatial statistics to study both the spatial distribution of wildfires during 2019 and their relationships to 15 environmental and anthropogenic factors. First, broad-scale spatial patterns of wildfire occurrence were explored using kernel density estimation, Moran’s I, Getis-Ord Gi*, and optimized hot spot analysis (OHSA). Second, the relationships between wildfire occurrence and the environmental and anthropogenic factors were explored using several regression models, including Ordinary Least Squares (OLS), global (quasi) Poisson, Geographically-weighted Gaussian Regression (GWGR), and Geographically-weighted Poisson Regression (GWPR). The spatial analysis results indicate that wildfires exhibited pronounced regional differences in spatial patterns in the vast and heterogeneous territory of the Amazônia Biome. The GWPR model outperformed the other regression models and explained the distribution and frequency of wildfires in the Amazônia Biome as a function of topographic, meteorologic, and environmental variables. Environmental factors like elevation, slope, relative humidity, and temperature were significant factors in explaining fire frequency in localized hotspots, while factors related to deforestation (forest loss, forest fragmentation measures, agriculture) explained wildfire activity over much of the region. Therefore, this study could improve a comprehensive study on, and understanding of, wildfire patterns and spatial variation in the target areas to support agencies as they prepare and plan for wildfire and land management activities in the Amazônia Biome.

The observed movements of humans and animals are realizations of complex spatiotemporal processes. Recent advances in location-aware technologies have rendered trajectory data ubiquitous. Examining the sequenced, instantaneous locations found in movement trajectory data for information reconstructing the location or state of the mover between observed points comprises a primary focus in Time Geography and related disciplines. The PySTPrism toolbox introduced in this paper provides a straightforward and open-source implementation of the Probabilistic Space Time Prism, in addition to related tools from Time Geography. PySTPrism is implemented in Python using the ArcPy module in ArcGIS Pro Desktop.

We hypothesized topographic features alone could be used to locate groundwater discharge, but only where diagnostic topographic signatures could first be identified through the use of limited field observations and geologic data. We built a geodatabase from geologic and topographic data, with the geologic data only covering ~40% of the study area and topographic data derived from airborne LiDAR covering the entire study area. We identified two types of groundwater discharge: shallow hillslope groundwater discharge, commonly manifested as diffuse seeps, and aquifer-outcrop groundwater discharge, commonly manifested as springs. We developed multistep manual procedures that allowed us to accurately predict the locations of both types of groundwater discharge in 93% of cases, though only where geologic data were available. However, field verification suggested that both types of groundwater discharge could be identified by specific combinations of topographic variables alone. We then applied maximum entropy modeling, a machine learning technique, to predict the prevalence of both types of groundwater discharge using six topographic variables: profile curvature range, with a permutation importance of 43.2%, followed by distance to flowlines, elevation, topographic roughness index, flow-weighted slope, and planform curvature, with permutation importance of 20.8%, 18.5%, 15.2%, 1.8%, and 0.5%, respectively. The AUC values for the model were 0.95 for training data and 0.91 for testing data, indicating outstanding model performance.

Finding clusters of events is an important task in many spatial analyses. Both confirmatory and exploratory methods exist to accomplish this. Traditional statistical techniques are viewed as confirmatory, or observational, in that researchers are confirming an a priori hypothesis. These methods often fail when applied to newer types of data like moving object data and big data. Moving object data incorporates at least three parts: location, time, and attributes. This paper proposes an improved space-time clustering approach that relies on agglomerative hierarchical clustering to identify groupings in movement data. The approach, i.e., space−time hierarchical clustering, incorporates location, time, and attribute information to identify the groups across a nested structure reflective of a hierarchical interpretation of scale. Simulations are used to understand the effects of different parameters, and to compare against existing clustering methodologies. The approach successfully improves on traditional approaches by allowing flexibility to understand both the spatial and temporal components when applied to data. The method is applied to animal tracking data to identify clusters, or hotspots, of activity within the animal’s home range.

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' 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' 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.