Applying Environmental Context to Rehabilitation Research Using Geographic Information Systems and Global Positioning Systems Geospatial Technologies.

BACKGROUND: The International Classification of Functioning, Disability, and Health includes important considerations of environmental context in understanding disability, but the environmental impact is often difficult to measure. PURPOSE: Demonstrates the use of Geographic Information Systems (GIS) and Global Positioning Systems (GPS) in rehabilitation research in assessing accessibility and participation; describes how to use these methods, and presents several considerations in using GIS and GPS in research. METHOD: Using methods from public health and medical geography, this article describes how to apply GIS and GPS technologies to rehabilitation research to measure community participation and accessibility to resources. FINDINGS: Directions for using ArcGIS functions and case examples joining these mapping technologies with rehabilitation measures are provided. CONCLUSIONS: Together with traditional measures, these technologies may provide rehabilitation researchers a more comprehensive approach to assessing accessibility and participation.

Leveraging Big Data and Analytics to Improve Food, Energy, and Water System Sustainability.

With the world population projected to grow significantly over the next few decades, and in the presence of additional stress caused by climate change and urbanization, securing the essential resources of food, energy, and water is one of the most pressing challenges that the world faces today. There is an increasing priority placed by the United Nations (UN) and US federal agencies on efforts to ensure the security of these critical resources, understand their interactions, and address common underlying challenges. At the heart of the technological challenge is data science applied to environmental data. The aim of this special publication is the focus on big data science for food, energy, and water systems (FEWSs). We describe a research methodology to frame in the FEWS context, including decision tools to aid policy makers and non-governmental organizations (NGOs) to tackle specific UN Sustainable Development Goals (SDGs). Through this exercise, we aim to improve the "supply chain" of FEWS research, from gathering and analyzing data to decision tools supporting policy makers in addressing FEWS issues in specific contexts. We discuss prior research in each of the segments to highlight shortcomings as well as future research directions.

Characterizing the Spatial Determinants and Prevention of Malaria in Kenya.

The United Nations' Sustainable Development Goal 3 is to ensure health and well-being for all at all ages with a specific target to end malaria by 2030. Aligned with this goal, the primary objective of this study is to determine the effectiveness of utilizing local spatial variations to uncover the statistical relationships between malaria incidence rate and environmental and behavioral factors across the counties of Kenya. Two data sources are used-Kenya Demographic and Health Surveys of 2000, 2005, 2010, and 2015, and the national Malaria Indicator Survey of 2015. The spatial analysis shows clustering of counties with high malaria incidence rate, or hot spots, in the Lake Victoria region and the east coastal area around Mombasa; there are significant clusters of counties with low incidence rate, or cold spot areas in Nairobi. We apply an analysis technique, geographically weighted regression, that helps to better model how environmental and social determinants are related to malaria incidence rate while accounting for the confounding effects of spatial non-stationarity. Some general patterns persist over the four years of observation. We establish that variables including rainfall, proximity to water, vegetation, and population density, show differential impacts on the incidence of malaria in Kenya. The El-Nino-southern oscillation (ENSO) event in 2015 was significant in driving up malaria in the southern region of Lake Victoria compared with prior time-periods. The applied spatial multivariate clustering analysis indicates the significance of social and behavioral survey responses. This study can help build a better spatially explicit predictive model for malaria in Kenya capturing the role and spatial distribution of environmental, social, behavioral, and other characteristics of the households.

Spatial heterogeneity of climate change as an experiential basis for skepticism.

We postulate that skepticism about climate change is partially caused by the spatial heterogeneity of climate change, which exposes experiential learners to climate heuristics that differ from the global average. This hypothesis is tested by formalizing an index that measures local changes in climate using station data and comparing this index with survey-based model estimates of county-level opinion about whether global warming is happening. Results indicate that more stations exhibit cooling and warming than predicted by random chance and that spatial variations in these changes can account for spatial variations in the percentage of the population that believes that "global warming is happening." This effect is diminished in areas that have experienced more record low temperatures than record highs since 2005. Together, these results suggest that skepticism about climate change is driven partially by personal experiences; an accurate heuristic for local changes in climate identifies obstacles to communicating ongoing changes in climate to the public and how these communications might be improved.

Accessibility patterns and community integration among previously homeless adults: a Geographic Information Systems (GIS) approach.

Although a desired rehabilitation goal, research continues to document that community integration significantly lags behind housing stability success rates for people of a variety of ages who used to be homeless. While accessibility to resources is an environmental factor that may promote or impede integration activity, there has been little empirical investigation into the impact of proximity of community features on resource use and integration. Using a Geographic Information Systems (GIS) approach, the current study examines how accessibility or proximity to community features in Boston, United States related to the types of locations used and the size of an individual's "activity space," or spatial presence in the community. Significant findings include an inverse relationship between activity space size and proximity to the number and type of community features in one's immediate area. Specifically, larger activity spaces were associated with neighborhoods with less community features, and smaller activity spaces corresponded with greater availability of resources within one's immediate area. Activity space size also varied, however, based on proximity to different types of resources, namely transportation and health care. Greater community function, or the ability to navigate and use community resources, was associated with better accessibility and feeling part of the community. Finally, proximity to a greater number of individual identified preferred community features was associated with better social integration. The current study suggests the ongoing challenges of successful integration may vary not just based on accessibility to, but relative importance of, specific community features and affinity with one's surroundings. Community integration researchers and housing providers may need to attend to the meaning attached to resources, not just presence or use in the community.

Measuring community integration using Geographic Information Systems (GIS) and participatory mapping for people who were once homeless.

Measures of community integration rely on self-report assessments that often quantify physical or social participation, but fail to capture the individual׳s spatial presence in the community. The current study documents the activity space, or area of daily experiences, of 37 individuals who were once homeless through participatory mapping and Geographic Information Systems (GIS). Contrary to expectations, there was no significant relationship between activity space size and community integration measures, except a negative association with physical integration. Further analysis revealed, however, that continued use of homeless services, geographically spread throughout the city, was associated with larger activity space size, but may be counterproductive to social and psychological integration efforts. Analysis of the types of locations identified revealed high importance given to leisure locations and ongoing involvement with medical and mental health locations. Finally, community integration outcomes did not differ significantly by demographics or housing type, but rather degree of family involvement and feeling like home, factors that may have more potential for change.

Social organization and genetic structure: insights from codistributed bat populations.

The impact of ecology and social organization on genetic structure at landscape spatial scales, where gene dynamics shape evolution as well as determine susceptibility to habitat fragmentation, is poorly understood. Attempts to assess these effects must take into account the potentially confounding effects of history. We used microsatellites to compare genetic structure in seven bat species with contrasting patterns of roosting ecology and social organization, all of which are codistributed in an ancient forest habitat that has been exceptionally buffered from radical habitat shifts. Over one thousand individuals were captured at foraging sites and genotyped at polymorphic microsatellite loci. Analyses of spatially explicit genotype data revealed interspecies differences in the extent of movement and gene flow and genetic structure across continuous intact forest. Highest positive genetic structure was observed in tree-roosting taxa that roost either alone or in small groups. By comparison, a complete absence of genetic autocorrelation was noted in the cave-roosting colonial species across the study area. Our results thus reveal measurable interspecies differences in the natural limits of gene flow in an unmodified habitat, which we attribute to contrasting roosting ecology and social organization. The consequences of ecology and behaviour for gene flow have important implications for conservation. In particular, tree-roosting species characterized by lower vagility and thus gene flow will be disproportionally impacted by landscape-scale forest clearance and habitat fragmentation, which are prevalent in the study region. Our method also highlights the usefulness of rapid sampling of foraging bats for assaying genetic structure, particularly where roosting sites are not always known.

Spatial analysis of malaria incidence at the village level in areas with unstable transmission in Ethiopia.

BACKGROUND: Malaria is the leading cause of morbidity and mortality in Ethiopia, accounting for over five million cases and thousands of deaths annually. The risks of morbidity and mortality associated with malaria are characterized by spatial and temporal variation across the country. This study examines the spatial and temporal patterns of malaria transmission at the local level and implements a risk mapping tool to aid in monitoring and disease control activities. METHODS: In this study, we examine the global and local patterns of malaria distribution in 543 villages in East Shoa, central Ethiopia using individual-level morbidity data collected from six laboratory and treatment centers between September 2002 and August 2006. RESULTS: Statistical analysis of malaria incidence by sex, age, and village through time reveal the presence of significant spatio-temporal variations. Poisson regression analysis shows a decrease in malaria incidence with increasing age. A significant difference in the malaria incidence density ratio (IDRs) is detected in males but not in females. A significant decrease in the malaria IDRs with increasing age is captured by a quadratic model. Local spatial statistics reveals clustering or hot spots within a 5 and 10 km distance of most villages in the study area. In addition, there are temporal variations in malaria incidence. CONCLUSION: Malaria incidence varies according to gender and age, with males age 5 and above showing a statistically higher incidence. Significant local clustering of malaria incidence occurs between pairs of villages within 1-10 km distance lags. Malaria incidence was higher in 2002-2003 than in other periods of observation. Malaria hot spots are displayed as risk maps that are useful for monitoring and spatial targeting of prevention and control measures against the disease.