Geospatial Factors Research Articles

A Geospatial Epidemiologic Analysis of Nontuberculous Mycobacterial Infection: An Ecological Study in Colorado.

Nontuberculous mycobacteria (NTM) are ubiquitous environmental microorganisms. Infection is thought to result primarily from exposure to soil and/or water sources. NTM disease prevalence varies greatly by geographic region, but the geospatial factors influencing this variation remain unclear. To identify sociodemographic and environmental ecological risk factors associated with NTM infection and disease in Colorado. We conducted an ecological study, combining data from patients with a diagnosis of NTM disease from National Jewish Health's electronic medical record database and ZIP code-level sociodemographic and environmental exposure data obtained from the U.S. Geological Survey, the U.S. Department of Agriculture, and the U.S. Census Bureau. We used spatial scan methods to identify high-risk clusters of NTM disease in Colorado. Ecological risk factors for disease were assessed using Bayesian generalized linear models assuming Poisson-distributed discrete responses (case counts by ZIP code) with the log link function. We identified two statistically significant high-risk clusters of disease. The primary cluster included ZIP codes in urban regions of Denver and Aurora, as well as regions south of Denver, on the east side of the Continental Divide. The secondary cluster was located on the west side of the Continental Divide in rural and mountainous regions. After adjustment for sociodemographic, drive time, and soil variables, we identified three watershed areas with relative risks of 12.2, 4.6, and 4.2 for slowly growing NTM infections compared with the mean disease risk for all watersheds in Colorado. This study population carries with it inherent limitations that may introduce bias. The lack of complete capture of NTM cases in Colorado may be related to factors such as disease severity, education and income levels, and insurance status. Our findings provide evidence that water derived from particular watersheds may be an important source of NTM exposure in Colorado. The watershed with the greatest risk of NTM disease contains the Dillon Reservoir. This reservoir is also the main water supply for major cities located in the two watersheds with the second and third highest disease risk in the state, suggesting an important possible source of infection.

Distribution of juvenile Pacific herring relative to environmental and geospatial factors in Prince William Sound, Alaska

Documenting distribution patterns of juvenile Pacific herring (Clupea pallasii) can clarify habitat preferences and provide insight into ecological factors influencing early life survival. However, few analyses relating juvenile Pacific herring density to habitat characteristics have been conducted. We sampled age-0 Pacific herring in nine bays and fjords distributed throughout Alaska's Prince William Sound during November over a 3-year period (2013–2015) and investigated associations between catch rate and habitat covariates using generalized linear mixed models. Our results indicated that the night-time distribution of age-0 Pacific herring in the pelagic environment was influenced by proximity to eelgrass (Zostera marina) beds, salinity, and water depth. Age-0 Pacific herring catch rate was negatively associated with tow depth, with herring favoring shallower water across the range of depths sampled (7.2–35.4m). In addition, Pacific herring distribution was positively associated with fresher water within the sampled salinity gradient (24.1–32.3 psu) and proximity to eelgrass beds. Seasonal changes in juvenile Pacific herring distribution were investigated by sampling one bay over a seven month period (October-April). Age-0 Pacific herring tended to remain in the inner bay region throughout the seven months, while age-1 Pacific herring had shifted from the inner to the outer bay by spring (March-April). Additionally, catch rate of age-0 Pacific herring in areas where ice breakup had just occurred was higher than in open water, suggesting that age-0 herring preferentially select ice-covered habitats when available. Based on our results we recommend that habitat preferences of age-0 Pacific herring should be considered in the development of Pacific herring year-class strength indices from catch data.

Impact of iron fortification on the geospatial patterns of malaria and non-malaria infection risk among young children: a secondary spatial analysis of clinical trial data from Ghana

ObjectivesPatterns of infection among children with varying levels of iron status in a malaria endemic area may vary spatially in ways requiring integrated infection and iron deficiency control programmes. The...

Geospatial modelling of flood susceptibility pattern in a subtropical area of West Bengal, India

Flood hazards are the most destructive among all natural disasters and are a constant threat to human’s life and property. Effective disaster risk reduction strategies can be improved by geospatial approach in the way of producing information and knowledge that are useful to plan truly effective actions for the protection from floods. This research aims to develop a quantified predictive model of flood susceptibility in the Ghatal and Tamluk subdivision of Medinipur district of West Bengal, India, by means of empirically selected and weighted spatial predictors of flood. The weighted prediction model is used to quantify the spatial associations between individual geospatial factors within the flood inundated study area. Yule’s coefficient and distance distribution analysis are used to assign weights to individual geo-factors, and finally weighted spatial predictors are integrated to a multi-class index overlay analysis to derive the spatially explicit predictive model of flood susceptibility. The resultant susceptibility model reveals that approximately 32.35 and 52.99% of the total study areas (3261.45 km2) are under the category of high-to-moderate flood susceptible zone. Quantitative results of this study could be integrated into the policy process in the formulation of local and national government plans for the future flood mitigation management and also to develop appropriate infrastructure in order to protect the lives and properties of the common people of the Medinipur district.

The impact of regular GP visits on diabetic potentially preventable hospitalisations

ABSTRACT
 ObjectivesPotentially preventable hospitalisations (PPHs) place a substantial burden on the Australian health system, with over 212,000 PPHs reported for diabetes alone in 2005/06. Timely and effective primary care may reduce the risk of acute episodes and subsequent hospitalisation among those with chronic diseases. The Medicare Enhanced Primary Care program, introduced to improve the regularity and quality of healthcare provided by GPs to Australians with chronic disease, has been shown to improve regularity of GP access. The aim of our study is to ascertain whether more regular GP access reduces diabetes PPHs.
 ApproachWhole of population longitudinal study using linked hospital, mortality, and general practice data. Regularity of GP access was determined through calculating the variance in the number of days between GP visits within a year. Regular GP contact was taken to indicate planned, proactive primary care, and irregular GP contact taken to indicate unplanned, reactive care. Multilevel modelling techniques were used to determine the relationship between regularity of GP access and diabetic PPHs in the population at risk of diabetes. Analyses were performed for the periods prior to and following the introduction of policies aimed at promoting primary care contact.
 ResultsThis paper will report results on the relationship between regular, proactive GP contact and diabetes PPHs in the population with and at risk of diabetes. Socio-demographic, geo-spatial and access factors were found to influence the regularity of GP access. Individual factors, in particular disease status, were found to substantially modify the relationship between regularity and PPH outcome. Findings differed between the periods prior to and following the introduction of government policies aimed at promoting proactive primary care in chronic disease.
 ConclusionFindings from this study will provide important evidence concerning strategies to reduce PPHs in relation to diabetes, which will be of interest to policy-makers wishing to reduce unnecessary hospitalisations. This work will extend to examine the impact of regularity on PPHs for a number of other chronic conditions considered priorities in Australia.

Health & access to care among working-age lower income adults in the Great Recession: Disparities across race and ethnicity and geospatial factors

In the United States (US) and elsewhere, residents of low resource areas face health-related disparities, and may experience different outcomes throughout times of severe economic flux. We aimed to identify individual (e.g. sociodemographic) and environmental (e.g. region, rurality) factors associated with self-reported health and forgone medical care due to the cost of treatment in the US across the Great Recession (2008–2009).We analyzed nationally representative data (2004–2010) using the Behavioral Risk Factor Surveillance System in the US. Individual and geospatial factors (rurality, census region) were used to identify differences in self-reported health and forgone medical care due to the cost.Adjusted-analyses taking into account individual and geospatial factors among those with incomes <$50,000 identified multiple differences across time, sex, education, disability, rurality and Census Region for health. Similar analyses for forgone medical care found that those in the Recovery and the Recession were more likely to report forgone care than before the Recession. Having insurance and/or being employed (versus unemployed) was a protective factor in terms of reporting fair/poor health and having to forgo health care due to cost.Policies affecting improvements in health and access for vulnerable populations (e.g., low-income minority adults) are critical. Monitoring trends related to Social Determinants of Health, including the relationship between health and place (e.g. Census region, rurality), is necessary in efforts targeted towards ameliorating disparities.

Demonstrating good practice in the safe operation of gas assets with predictive analytics

The oil and gas industry operates large and complex facilities. Technical integrity (and thus licence to operate) must be maintained through routine inspection and maintenance regimes. Corrosion attacks every component at every stage in the life of every oil and gas field or plant (Schlumberger 1994). Globally, corrosion management accounts for US2.5Tr cross-industry spend (NACE International 2016). Risk-based approaches for internal corrosion based on susceptibility of a process item to corrode, have been utilised to assist with identifying appropriate and more cost-effective maintenance and inspection strategies. The aim of such approaches is to protect integrity and not compromise safety; however, they do nothing to minimise regret cost. These approaches use only known physical characteristics of piping equipment and rely on repeat inspection data to calculate corrosion rates and associated maintenance schedules. The present paper will leverage the challenges and shortcomings of using existing risk-based inspection (RBI) approaches and demonstrate how Accenture in collaboration with Woodside and others is utilising predictive analytics to more accurately determine likelihood of corrosion to exist in a more granular resolution, thus managing likelihood and consequence of corrosion to produce an improved risk-based model. The analytics model considers physical, geospatial and external factors for external corrosion. This is a work in progress, with very promising initial results, that leads into the implementation of an improved RBI strategy, enabling Woodside to reduce inspection scope, physical site activity and associated management cost. In addition, it better manages plant risk in conjunction with smart visualisation tools.

Emergence of geo-enabled research in social sciences: The case of societal higher education issues

The rapid expansion of global trends and the internationalization of higher education are increasingly requiring the consideration of the geospatial factor for better-informed inquiries on the related societal challenges. The article’s findings point to the need to overcome education researchers’ general lack of interest in the geospatial dimension because it offers great opportunities for social scientists in a wide range of disciplines to take advantage of geo-enabled research capabilities to spatially analyze socio-educational issues and thus to benefit from the availability of an emerging perspective for enriching the current methodological practices in the social sciences in general.

Geo-spatial factors associated with infection risk among young children in rural Ghana: a secondary spatial analysis.

BackgroundDetermining the spatial patterns of infection among young children living in a malaria-endemic area may provide a means of locating high-risk populations who could benefit from additional resources for treatment and improved access to healthcare. The objective of this secondary analysis of baseline data from a cluster-randomized trial among 1943 young Ghanaian children (6–35 months of age) was to determine the geo-spatial factors associated with malaria and non-malaria infection status.MethodsSpatial analyses were conducted using a generalized linear geostatistical model with a Matern spatial correlation function and four definitions of infection status using different combinations of inflammation (C-reactive protein, CRP > 5 mg/L) and malaria parasitaemia (with or without fever). Potentially informative variables were included in a final model through a series of modelling steps, including: individual-level variables (Model 1); household-level variables (Model 2); and, satellite-derived spatial variables (Model 3). A final (Model 4) and maximal model (Model 5) included a set of selected covariates from Models 1 to 3.ResultsThe final models indicated that children with inflammation (CRP > 5 mg/L) and/or any evidence of malaria parasitaemia at baseline were more likely to be under 2 years of age, stunted, wasted, live further from a health facility, live at a lower elevation, have less educated mothers, and higher ferritin concentrations (corrected for inflammation) compared to children without inflammation or parasitaemia. Similar results were found when infection was defined as clinical malaria or parasitaemia with/without fever (definitions 3 and 4). Conversely, when infection was defined using CRP only, all covariates were non-significant with the exception of baseline ferritin concentration. In Model 5, all infection definitions that included parasitaemia demonstrated a significant interaction between normalized difference vegetation index and land cover type. Maps of the predicted infection probabilities and spatial random effect showed defined high- and low-risk areas that tended to coincide with elevation and cluster around villages.ConclusionsThe risk of infection among young children in a malaria-endemic area may have a predictable spatial pattern which is associated with geographical characteristics, such as elevation and distance to a health facility.Original trial registration clinicaltrials.gov (NCT01001871)Electronic supplementary materialThe online version of this article (doi:10.1186/s12936-016-1388-1) contains supplementary material, which is available to authorized users.

Reprecincting and Voting Behavior

Despite the expansion of convenience voting across the American states, millions of voters continue to cast ballots at their local precincts on Election Day. We argue that those registered voters who are reassigned to a different Election Day polling place prior to an election are less likely to turn out to vote than those assigned to vote at the same precinct location, as a new precinct location incurs both search and transportation costs on reassigned voters. Utilizing voter file data and precinct shape files from Manatee County, Florida, from before and after the 2014 General Election, we demonstrate that the redrawing of precinct boundaries and the designation of Election Day polling places is not a purely technical matter for local election administrators, but may affect voter turnout of some registered voters more than others. Controlling for a host of demographic, partisan, vote history, and geospatial factors, we find significantly lower turnout among registered voters who were reassigned to a new Election Day precinct compared to those who were not, an effect not equally offset by those voters turning to other available modes of voting (either early in-person or absentee). All else equal, we find that registered Hispanic voters were significantly more likely to abstain from voting as a result of being reassigned than any other racial group.

Survival analysis of geospatial factors in the Irish ALS cohort

Variations in environmental risk factors potentially influence incidence and progression in complex multifactorial diseases. Few studies have examined the association of survival in amyotrophic lateral sclerosis (ALS) with environmental geospatial variables. Here we use data from the Irish ALS cohort to perform such an analysis. Geographic data sources were used to generate small area values for four geospatial variables (population density, social deprivation, distance to coast, and distance to ALS multidisciplinary (MDT) clinic) for each ALS case on the Irish ALS register. These were combined with follow-up data and used as covariates in Royston-Parmar regression survival analysis including age of onset, site of onset, diagnostic delay, riluzole prescription and MDT clinic attendance as covariates. One thousand, two hundred and thirty-two patients with median survival of 2.31 years from disease onset were included. After addition of the individual geospatial variables in turn, none of the four variables was found to be associated with survival with a p-value <0.05. The results may reflect the public healthcare system that provides riluzole prescription and access to the MDT to all patients free of charge, and is also congruent with our recent finding that social deprivation is not associated with ALS incidence in Ireland.

Protocol for a population-based molecular epidemiology study of tuberculosis transmission in a high HIV-burden setting: the Botswana Kopanyo study

IntroductionMycobacterium tuberculosis (Mtb) is transmitted from person to person via airborne droplet nuclei. At the community level, Mtb transmission depends on the exposure venue, infectiousness of the tuberculosis (TB) index...

Enhanced Geospatial Validity for Meta-analysis and Environmental Benefit Transfer: An Application to Water Quality Improvements

Meta-regression models are commonly used within benefit transfer to estimate willingness to pay (WTP) for environmental quality improvements. Theory suggests that these estimates should be sensitive to geospatial factors including resource scale, market extent, and the availability of substitutes and complements. Valuation meta-regression models addressing the quantity of non-market commodities sometimes incorporate spatial variables that proxy for a subset of these effects. However, meta-analyses of WTP for environmental quality generally omit geospatial factors such as these, leading to benefit transfers that are invariant to these factors. This paper reports on a meta-regression model for water quality benefit transfer that incorporates spatially explicit factors predicted by theory to influence WTP. The metadata are drawn from stated preference studies that estimate per household WTP for water quality changes in United States water bodies, and combine primary study information with extensive geospatial data from external sources. Results find that geospatial variables are associated with significant WTP variations as predicted by theory, and that inclusion of these variables reduces transfer errors.

Determinants of Quality Care and Mortality for Patients With Locally Advanced Cervical Cancer in Virginia.

Outcomes for patients with locally advanced cervical cancer are influenced by receipt of all indicated components of quality care: early diagnosis and receipt of external beam radiation therapy, chemotherapy, and brachytherapy. We performed an observational cohort study to evaluate receipt of quality cancer care and mortality after cancer diagnosis among patients with locally advanced cervical cancer in Virginia.We queried the Virginia state cancer registry to identify patients with International Federation of Gynecology and Obstetrics Stage IB-IVA cervical cancer who were diagnosed during 2002 to 2012. We evaluated the influence of tumor-related, demographic, and geospatial factors on the receipt of indicated therapies and mortality. Treatment quality score of 0 to 3 was defined based upon the extent of receipt of the components of indicated therapy.A total of 1048 patients were identified; 33.1% received all 3 components of treatment and only 54.0% received brachytherapy. Predictors of higher quality score included younger age group versus 66+ years at diagnosis (18–42 odds ratio [OR] = 12.3, 95% confidence interval: 6.6, 23.0; 42–53 OR = 5.6, CI: 3.3, 9.5; 53–66 OR = 5.5, CI: 3.3, 9.1), lower tumor stages versus IVA (IB2 OR = 3.3, CI: 1.8, 6.2; II OR = 2.7, CI: 1.6, 4.5; IIIx OR = 2.1, CI: 1.3, 3.6), and treatment at a high-volume facility (OR 2.2, CI: 1.2, 4.2). Predictors of increased mortality included earlier year of diagnosis, higher tumor stage, treatment at a lower volume facility, and lower treatment quality score.In a cohort of locally advanced cervical cancer patients in Virginia, we identified a low rate of receipt of complete quality care for cervical cancer and a strong effect of facility volume on quality treatment and survival. Further research is needed to develop strategies to improve access to quality treatment and outcomes for cervical cancer.

The City as the Nexus Model: Bridging the State, Market, Societal, and Geospatial Contexts

Cities serve as nexus, they connect and are shaped by politico-governmental (State), market-economic (Market), civil-societal (Societal), and geographic-natural (Geo-spatial) activities and/or events occurring at all spatial tiers. As a result, a thorough understanding of urban development outcomes requires a trans-disciplinary, integrated approach that draws upon multiple, even sometimes competing, scholarly paradigms. In the spirit of this, this article introduces The City as the Nexus Model, a new perspective for analyzing urban and regional development trajectories, which incorporates the ideas of Agglomeration, Urban Regime, World/Global City, and Nested City Theories, among others. By drawing upon the central themes of these sometimes competing perspectives, this new model shows how a combination of State, Market, Societal, and Geospatial factors uniquely shape economic and spatial outcomes in the world's city-regions. Most importantly, the article/new model provides scholars, practitioners, and students with a new trans-disciplinary framework to utilize in their own empirically-based studies of city-regions.

Statistical Validation of a Web-Based GIS Application and Its Applicability to Cardiovascular-Related Studies

Purpose: There is abundant evidence that neighborhood characteristics are significantly linked to the health of the inhabitants of a given space within a given time frame. This study is to statistically validate a web-based GIS application designed to support cardiovascular-related research developed by the NIH funded Research Centers in Minority Institutions (RCMI) Translational Research Network (RTRN) Data Coordinating Center (DCC) and discuss its applicability to cardiovascular studies. Methods: Geo-referencing, geocoding and geospatial analyses were conducted for 500 randomly selected home addresses in a U.S. southeastern Metropolitan area. The correlation coefficient, factor analysis and Cronbach’s alpha (α) were estimated to quantify measures of the internal consistency, reliability and construct/criterion/discriminant validity of the cardiovascular-related geospatial variables (walk score, number of hospitals, fast food restaurants, parks and sidewalks). Results: Cronbach’s α for CVD GEOSPATIAL variables was 95.5%, implying successful internal consistency. Walk scores were significantly correlated with number of hospitals (r = 0.715; p < 0.0001), fast food restaurants (r = 0.729; p < 0.0001), parks (r = 0.773; p < 0.0001) and sidewalks (r = 0.648; p < 0.0001) within a mile from homes. It was also significantly associated with diversity index (r = 0.138, p = 0.0023), median household incomes (r = −0.181; p < 0.0001), and owner occupied rates (r = −0.440; p < 0.0001). However, its non-significant correlation was found with median age, vulnerability, unemployment rate, labor force, and population growth rate. Conclusion: Our data demonstrates that geospatial data generated by the web-based application were internally consistent and demonstrated satisfactory validity. Therefore, the GIS application may be useful to apply to cardiovascular-related studies aimed to investigate potential impact of geospatial factors on diseases and/or the long-term effect of clinical trials.

Market Competition and Density in Liver Transplantation: Relationship to Volume and Outcomes

Liver transplantation centers are unevenly distributed within the Donor Service Areas (DSAs) of the United States. This study assessed how market competition and liver transplantation center density are associated with liver transplantation volume within individual DSAs. We conducted a retrospective cohort study of 53,156 adult liver transplants in 45 DSAs with 110 transplantation centers identified from the Scientific Registry of Transplant Recipients between 2003 and 2012. The following measures were derived annually for each DSA: market competition using the Herfindahl Hirschman Index, transplantation center density by the Average Nearest Neighbor method, liver quality by the Liver Donor Risk Index, and patient risk by the Model for End-Stage Liver Disease. A hierarchical mixed effects negative binomial regression model of the relationship between liver transplants and market factors was created annually. Patient and graft survival were investigated with a Cox proportional hazards model. Transplantation center density was associated with market competition (p < 0.0001), listings for organ transplantation (p < 0.0001), and Model for End-Stage Liver Disease at transplantation (p = 0.0005). More liver transplantation centers (incidence rate ratio [IRR] = 1.03; p = 0.04), greater market competition (IRR = 1.36; p = 0.02), increased listings (IRR = 1.14; p < 0.0001), more donors (IRR = 1.24; p < 0.0001), and higher Liver Donor Risk Index (IRR = 3.35; p < 0.0001) were associated with more transplants. No market variables were associated with increased mortality after transplantation. After controlling for demographic and market factors, a greater concentration of centers was associated with more liver transplants without impacting overall survival. These results warrant additional investigation into the relationship between geospatial factors and liver transplantation volume with consideration for the optimization of scarce resources.

A susceptibility mapping model of earthquake-triggered slope geohazards based on geo-spatial data in mountainous regions

The main purpose of this paper is to develop a susceptibility mapping model of earthquake-trigged slope geohazards based on geo-spatial data in mountainous region. A rapid-response mapping model of slope geohazards triggered by earthquakes that can be widely applied in practice can be constructed by a simple method of weighted superposition of related accessible factors. Based on site investigations into geological disasters in Wenchuan County caused by the Wenchuan earthquake on 12 May 2008, the main influencing factors of the earthquake triggering slope geohazards were studied, including peak ground acceleration (PGA) and 6 geo-spatial data factors: distance from streams, distance from highway, slope gradient, slope position, normalised difference vegetation index (NDVI) and micro-landforms. The order of the relative importance of each factor was determined by regression analysis, then the weight of each factor contributing to slope geohazards was calculated by using the analytic hierarchy process (AHP). The susceptibility mapping model of slope geohazards was derived from the map overlaying principle in ArcGIS platform, and finally, the results were re-classified to obtain the map concerning susceptibility areas of slope geohazards. The results, by comparing the geohazards points with the field survey, proved that over 63% geohazards points were located in high and very high susceptibility areas, while only about 14% geohazards points were located in very low and low susceptibility areas. The model showed a relative agreement between the points on the susceptibility mapping and onsite points of slope geohazards. Above all, predictions about the slope geohazards at meizoseismal areas of earthquakes occurring in mountainous regions can be based on geo-spatial data; so the model could be applied to study other areas which may be prone to the slope geohazards during the earthquakes.

GIS and Remote Sensing for Malaria Risk Mapping, Ethiopia

Abstract. Integrating malaria data into a decision support system (DSS) using Geographic Information System (GIS) and remote sensing tool can provide timely information and decision makers get prepared to make better and faster decisions which can reduce the damage and minimize the loss caused. This paper attempted to asses and produce maps of malaria prone areas including the most important natural factors. The input data were based on the geospatial factors including climatic, social and Topographic aspects from secondary data. The objective of study is to prepare malaria hazard, Vulnerability, and element at risk map which give the final output, malaria risk map. The malaria hazard analyses were computed using multi criteria evaluation (MCE) using environmental factors such as topographic factors (elevation, slope and flow distance to stream), land use/ land cover and Breeding site were developed and weighted, then weighted overlay technique were computed in ArcGIS software to generate malaria hazard map. The resulting malaria hazard map depicts that 19.2 %, 30.8 %, 25.1 %, 16.6 % and 8.3 % of the District were subjected to very high, high, moderate, low and very low malaria hazard areas respectively. For vulnerability analysis, health station location and speed constant in Spatial Analyst module were used to generate factor maps. For element at risk, land use land cover map were used to generate element at risk map. Finally malaria risk map of the District was generated. Land use land cover map which is the element at risk in the District, the vulnerability map and the hazard map were overlaid. The final output based on this approach is a malaria risk map, which is classified into 5 classes which is Very High-risk area, High-risk area, Moderate risk area, Low risk area and Very low risk area. The risk map produced from the overlay analysis showed that 20.5 %, 11.6 %, 23.8 %, 34.1 % and 26.4 % of the District were subjected to very high, high, moderate, low and very low malaria risk respectively. This help to plan valuable measures to be taken in early warning, monitor, control and prevent malaria epidemics.

The influence of geospatial factors on democracy: Its representation on web interface design

Democracy is represented on web interface design (Li, 2010). Wittfogel's (1957) Eastern autocracy states that 2 environmental dimensions, rainfall and sea border, influence the origin of democracy. This study examined Wittfogel's Eastern autocracy theory through statistical analysis of average annual precipitation, land boundaries, latitudes, and annual temperature of 196 countries and territories with their freedom levels defined by Freedom House, to find out the correlations between these geospatial factors and democracy. In addition, this study extended its investigation to web interface design by examining democracy represented on college/university websites in correlations with these geospatial factors. A total of 130 college/university websites selected from 65 countries were coded and examined systematically in linear and multiple regression analyses. This study concluded that democracy correlates positively with annual precipitation and latitude, but negatively with land boundaries and annual temperature. Furthermore, this study indicated that these 4 geospatial variables associate with democracy represented on web interface design, although the associations are not statistically significant. This study also suggested that it is more accurate to predict democracy if the 4 geospatial factors are considered together as dependent variables. By examining Wittfogel's theory of hydraulic civilization on web interface design, this study not only extended its sociological perspective to the information science arena, but also provided a better understanding of the functionality of the Internet in information dissemination and its cultural and sociological aspects.