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This book focuses on a range of geospatial applications for environmental health research, including environmental justice issues, environmental health disparities, air and water contamination, and infectious diseases. Environmental health research is at an exciting point in its use of geotechnologies, and many researchers are working on innovative approaches. This book is a timely scholarly contribution in updating the key concepts and applications of using GIS and other geospatial methods for environmental health research. Each chapter contains original research which utilizes a geotechnical

The increasingly urbanized world has created various problems of environment, climate, consumption of resources, and public health, which are closely linked to the side-effects of urbanization such as sprawl, congestion, housing affordability and loss of open space. Fundamental to the urban problems are two separate yet related issues: urban structure and urban dynamics. The chapters collected in this book present an excellent profile of the current state of geospatial analysis and modelling, and demonstrate how these approaches can contribute to the study of various urban issues. The book add.

Abstract
Vegetation cover over Nigeria has been on the decrease recently, hence the need for adequate monitoring using geo-information technology. This study examined the spatio-temporal variation of vegetation cover over Nigeria for thirty years with a view to developing a strategy for enhancing environmental sustainability. In order to predict the spatial extent of vegetation cover in 2030, the study utilised satellite images from between 1981 and 2010 using the Normalised Difference Vegetation Index (NDVI) coupled with cellular automata and Markov chain techniques in ArcGIS 10.3. The results showed that dense vegetal areas decreased in area from 358,534.2 km2 in 1981 to 207,812 km2 in 2010, while non-vegetal areas increased from 312,640.8 km2 in 1981 to 474,436.4 km2 in 2010 with a predicted increase to 501,504.9 km2 by 2030, i.e. an increase of about 27,068.4 km2 between 2010 and 2030. The study concluded that geoinformation techniques are effective in monitoring long-term intra- and inter-annual variability of vegetation and also useful in developing sustainable strategies for combating ecological hazards.

Assessment of irrigated lands by conventional means of survey requires a great deal of time, but the application of geospatial analysis using remote sensing data and GIS techniques minimize time consuming and offer the possibility rapid production of maps and models. This paper gave an overview of the techniques and methods in use at different scales. The presence of salt in the soils and its variation may be because of rise in water table and the difference in elevation in irrigated lands. The combined application of conventional methods with remote sensing and geographical information system techniques in detecting these problems in irrigated lands were examined. Different salinity indexes coupled with ground truthing with the proven results in assessing such problems were also examined thereby depicting indexes as good indicator of soil salinity and water logging, which may influence decision on reclamation of degraded land for proper agricultural land management. Irrigation and drainage managers, planners, farmers, and government agencies for smart agriculture can use models and maps generated through geospatial analysis.

Wohnen in städtischen Zentren ist sowohl politisch als auch im Marktkontext ein heikles Thema. Dies gilt insbesondere für den Wohnungsmarkt in Berlin, wo weit verbreitete Spekulationen und internationale Investitionen in kurzer Zeit zu einem starken Anstieg der Immobilien- und Mietkosten geführt haben. Für alle Beteiligten in dieser Gleichung (Politiker, Anwohner, Immobilieninvestoren / Entwickler) ist es wichtig zu verstehen, was Preis und Nachfrage in diesem einzigartigen Umfeld antreibt. Um diese Dynamik zu modellieren, verwendet diese Arbeit eine geographically-weighted Regression (GWR) und erweitert die hedonische Preismethode (HPM) um Merkmale wie Beliebtheit einer Nachbarschaft, Zugang zur Natur (Parks, Flüsse, usw.), Zugang zu öffentlichen Verkehrsmitteln, und Nähe zu Einkaufsmöglichkeiten und Einkaufsmöglichkeiten Schulen, unter anderem. Google Ortsdaten und Rezensionen repräsentieren die allgemeine Beliebtheit von Standorten. Öffentliche Daten des Berliner Senats und des VBB liefern räumliche Informationen zu natürlichen Ressourcen bzw. zum Zugang zu öffentlichen Verkehrsmitteln. Kernel-Dichteschätzung (KDE) wird verwendet, um räumliche Muster und die Verteilung von Orten von Interesse (POIs) zu analysieren, wobei die Hotspot-Analyse unter Verwendung der Getis-Ord-Statistik durchgeführt wird. Die Immobiliendaten stammen aus einem Datensatz von Buchungen, die auf einer lokalen Plattform für möblierte Mietwohnungen vorgenommen wurden. Die GWR-Ergebnisse zeigen eine hohe Korrelation zwischen POI-Clustering und Preis sowie signifikante Verbesserungen der Modellleistung. Dem GWR gelingt es insbesondere, die räumliche Heterogenität von Merkmalseffekten zu erfassen, die von der globalen OLS-Regression übersehen werden. Diese Studie bietet eine einzigartige Verbindung von öffentlichen und privaten Datenquellen, um zu einer neuartigen Analyse des Berliner Immobilienmarktes zu gelangen. ; Housing in urban centres is a sensitive issue both politically and in a market context. This is particularly true for the housing market in Berlin, Germany, where widespread speculation and international investment have resulted in sharp increases in property and rental costs in a short period of time. For all stakeholders in this equation (politicians, residents, property investors/developers), it is important to understand what drives price and demand in this unique environment. To model these dynamics, this thesis employs a geographically weighted regression(GWR) and extends the hedonic pricing method (HPM) to include features involving neighbourhood popularity, access to nature (parks, rivers), access to public transportation, and proximity to shopping and schools, among other kinds of locations. Google place data & review counts represent general popularity of locations. Public data from the Berlin Senate and the VBB provide geospatial information on natural resources and access to public transportation, respectively. Kernel density estimation (KDE) is employed to analyse spatial patterns and distribution of places of interest (POIs), with hotspot analysis performed using the Getis-Ord statistic. Property data comes from a dataset of bookings made on a local platform for furnished apartment rentals. GWR results show a high correlation between POI clustering and price as well as significant improvements in model performance. The GWR notably succeeds in capturing spatial heterogeneity in feature effects that global OLS regression overlooks. This study provides a unique marriage of public and private data sources to arrive at a novel analysis of the Berlin property market.

At the intersection of geography and public health, the field of spatial epidemiology seeks to use the tools of geospatial analysis to answer questions about disease. In this work we explore two areas: the use of geostatistical modeling as an extension of niche modeling, and the use of mobility metrics to augment modeling for epidemic responses. Niche modeling refers to the practice of using statistical methods to relate the underlying spatially distributed environmental variables to an outcome, typically presence or absence of a species. Such work is common in disease ecology, and often focuses on exploring the range of a disease vector or pathogen. The technique also allows one to explore the importance of each underlying regressor, and the effect it has on the outcome. We demonstrate that this concept can be extended, through geostatistical modeling, to explore non-logistic phenomena such as incidence. When combined with weather forecasts, such efforts can even predict incidence of an upcoming season, allowing us to estimate the total number of expected cases, and where we would expect to find them. We demonstrate this in Chapter 2, by forecasting the incidence of melioidosis in Australia given weather forecasts a year prior. We also evaluate the efficacy of this technique and explore the impact of environmental variables such as elevation on melioidosis. But these techniques are not limited to free-living and vector-borne pathogens. We theorize that they can also be applied to diseases that spread exclusively by person-to-person contact. Exploring this allows us to find areas of underreporting, as well as areas with unusual local forcing which might merit further investigation by the health department. We also explore this in Chapter 4, by relating the incidence of hepatitis C in rural Virginia to demographic data. The West African Ebola Outbreak of 2014 demonstrated the need to include mobility in predictive disease modeling. One can no longer assume that neglected tropical diseases will remain contained and immobile, and the assumption of random mixing across large areas is unwise. Our efforts with modeling mobility are twofold. In Chapter 3, we demonstrate the creation of mobility metrics from open source road and river network data. We then demonstrate the usefulness of such data in a meta-population patch model meant to forecast the spread of Ebola in the Democratic Republic of Congo. In Chapter 4, we also demonstrate that mobility data can be used to strengthen outbreak detection via hotspot analysis, and to augment incidence models by factoring in the incidence rates of neighboring areas. These efforts will allow health departments to more accurately forecast incidence, and more readily identify disease hotspots of atypical size and shape. ; Doctor of Philosophy ; The focus of this work is called spatial epidemiology, which combines geography with public health, to answer the where, and why, of disease. This is a growing field, and youve likely seen it in the news and media. Have you ever seen a map of the United States turning red in some virus disaster movie? The real thing looks a lot like that. After the Ebola outbreak of 2014, public health agencies wanted to know where the next one might hit. Now that there is another outbreak, we need to ask where and how will it spread? What areas are hardest hit, and how bad is it going to get? We can answer all these questions with spatial epidemiology. Our work adds to two aspects of spatial epidemiology: niche modeling, and mobility. We use niche modeling to determine where we could find certain diseases, usually those that are spread by insects or animals. Consider Lyme disease, you get it from the bite of a tick, and the tick gets it from a white-footed mouse. But both the mice and ticks only live in certain parts of the country. With niche modeling we can determine where those are, and we can also guess at what makes those areas attractive to the mice and ticks. Is it winter harshness, summer temperatures, rainfall, and/or elevation? Is it something else? In Chapter 2, we show that you can extend this idea. Instead of just looking at where the disease is, what if we could guess how many people will get infected? What if we could do so, a year in advance? We show that this can be done, but we need a good idea of what the weather will be like next year. In Chapter 4, we show that you can do the same thing with hepatitis C. Instead of Lymes ticks and mice, hepatitis C depends on drug-use, unregulated tattooing, and unsafe sex. And like with Lyme, these things are only found in certain places. Instead of temperature or rainfall, we now need to find areas with drug-problems and poverty. But we can get an idea of this from the Census Bureau, and we can make a map of hepatitis C as easily as we did for Lyme. But hepatitis C spreads person-to-person. So, we need some idea of how people move around the area. This is where mobility comes in. Mobility is important for most public health work, from detecting outbreaks to estimating where the disease will spread next. In Chapter 3, we show how one could create a mobility model for a rural area where few maps exist. We also show how to use that model to guess where the next cases of Ebola will show up. In Chapter 4, we show how you could use mobility to improve outbreak and hotspot detection. We also show how its used to help estimate the number of cases in an area. Because that number depends on how many cases are imported from the surrounding areas. And the only way to estimate that is with mobility.