Neighborhood disparities in stroke and myocardial infarction mortality: a GIS and spatial scan statistics approach

<p>Abstract</p> <p>Background</p> <p>Stroke and myocardial infarction (MI) are serious public health burdens in the US. These burdens vary by geographic location with the highest mortality risks reported in the southeastern US. While these disparities have been investigated at state and county levels, little is known regarding disparities in risk at lower levels of geography, such as neighborhoods. Therefore, the objective of this study was to investigate spatial patterns of stroke and MI mortality risks in the East Tennessee Appalachian Region so as to identify neighborhoods with the highest risks.</p> <p>Methods</p> <p>Stroke and MI mortality data for the period 1999-2007, obtained free of charge upon request from the Tennessee Department of Health, were aggregated to the census tract (neighborhood) level. Mortality risks were age-standardized by the direct method. To adjust for spatial autocorrelation, population heterogeneity, and variance instability, standardized risks were smoothed using Spatial Empirical Bayesian technique. Spatial clusters of high risks were identified using spatial scan statistics, with a discrete Poisson model adjusted for age and using a 5% scanning window. Significance testing was performed using 999 Monte Carlo permutations. Logistic models were used to investigate neighborhood level socioeconomic and demographic predictors of the identified spatial clusters.</p> <p>Results</p> <p>There were 3,824 stroke deaths and 5,018 MI deaths. Neighborhoods with significantly high mortality risks were identified. Annual stroke mortality risks ranged from 0 to 182 per 100,000 population (median: 55.6), while annual MI mortality risks ranged from 0 to 243 per 100,000 population (median: 65.5). Stroke and MI mortality risks exceeded the state risks of 67.5 and 85.5 in 28% and 32% of the neighborhoods, respectively. Six and ten significant (p < 0.001) spatial clusters of high risk of stroke and MI mortality were identified, respectively. Neighborhoods belonging to high risk clusters of stroke and MI mortality tended to have high proportions of the population with low education attainment.</p> <p>Conclusions</p> <p>These methods for identifying disparities in mortality risks across neighborhoods are useful for identifying high risk communities and for guiding population health programs aimed at addressing health disparities and improving population health.</p

Quality assessment of online street and rooftop geocoding services

The widespread use of Internet-based mapping and geospatial analysis has caused an increase in the demand for online geocoding services. Although such services provide convenience, low (or free) cost and immediate solutions, their characteristics, sometimes, overshadow the expectation of producing quality of geocoded results. In recent years, several geocoding techniques have emerged, including rooftop geocoding, but they have yet to receive much attention in the literature. This paper examines and compares the quality of online rooftop and street geocoding services based on match rates and positional accuracy. Six geocoding services by five providers (i.e., Microsoft Virtual Earth, Google, Geocoder.us, MapQuest, and Yahoo!) were evaluated using addresses in Allegheny County, Pennsylvania. Results of the comparison indicate that rooftop geocoding produces slightly lower match rates but significantly higher positional accuracy than street geocoding. The hybrid service, which combines the two techniques, produces match rates as high as other street geocoding services but improves in positional accuracy close to the level of rooftop geocoding. Geocoding services employing reference databases with similar quality trend to produce compatible match rates and positional accuracy. This paper examines the sensitivity of different address types on geocoding quality. The results reveal that both rooftop and street geocoding produce high match rates and high accuracy for residential addresses. However, positional accuracies of agricultural and industrial address types are not very reliable due to the small sample sizes. With these, it is recommended to use online rooftop geocoding services if high positional accuracy is the priority, use street geocoding if high match rate is the priority, and use the hybrid approach if both high match rates and high positional accuracy are required

C2GEO: Techniques and tools for real-time data-intensive geoprocessing in cloud computing

Interest in implementing and deploying many existing and new applications on cloud platforms is continually growing. Of these, geospatial applications, whose operations are based on geospatial data and computation, are of particular interest because they typically involve very large geospatial data layers and specialized and complex computations. In general, problems in many geospatial applications, especially those with real-time response, are compute- and/or data-intensive, which is the reason why researchers often resort to high-performance computing platforms for efficient processing. However, compared to existing high-performance computing platforms, such as grids and supercomputers, cloud computing offers new and advanced features that can benefit geospatial problem solving and application implementation and deployment. In this paper, we present a distributed algorithm for geospatial data processing on clouds and discuss the results of our experimentation with an existing cloud platform to evaluate its performance for real-time geoprocessing

Geocoding recommender: An algorithm to recommend optimal online geocoding services for applications

Today, many services that can geocode addresses are available to domain scientists and researchers, software developers, and end-users. For a number of reasons, including quality of reference database and interpolation technique, a given address geocoded by different services does not often result in the same location. Considering that there are many widely available and accessible geocoding services and that each geocoding service may utilize a different reference database and interpolation technique, selecting a suitable geocoding service that meets the requirements of any application or user is a challenging task. This is especially true for online geocoding services which are often used as black boxes and do not provide knowledge about the reference databases and the interpolation techniques they employ. In this article, we present a geocoding recommender algorithm that can recommend optimal online geocoding services by realizing the characteristics (positional accuracy and match rate) of the services and preferences of the user and/or their application. The algorithm is simulated and analyzed using six popular online geocoding services for different address types (agricultural, commercial, industrial, residential) and preferences (match rate, positional accuracy). © 2011 Blackwell Publishing Ltd

Grid-based geoprocessing for integrated global navigation satellite system simulation

It is anticipated that the integration of global navigation satellite systems (GNSS) will improve the overall positioning performance benefiting many existing applications and paving the way for the emergence of new location-based or location-aware applications. However, such integrations of GNSSs (iGNSS) will also require the development of a new generation of receivers and methodologies that can utilize the new information efficiently and effectively. This is especially true for real-time applications, such as navigation, for which time performance is of the essence in providing highly accurate positioning solutions. In this paper, we address time performance of iGNSS by focusing only on the visibility parameter, among all parameters, because the visibility solution is a prerequisite for the other parameters, and it is very expensive computationally. We propose an efficient web service for visibility computation, called iGNSS-v and discuss our experimentation with a grid computing platform. The results of the experiment indicate that grids can potentially benefit iGNSS by improving the time performances of its various components, but further optimization is needed in order to address the real-time requirement of iGNSS-QoS prediction. © 2012 American Society of Civil Engineers

Personalized accessibility map (PAM): a novel assisted wayfinding approach for people with disabilities

Accessibility information is necessary to support the everyday mobility of people with disabilities. As a service to aid the mobility of students with disabilities, some universities and colleges provide maps with accessibility information for their campus on the Web. Other maps, while not focused specifically on students, provide information about indoor accessibility and can be extended by users. In this paper, accessibility requirements published in the literature, the criteria used in existing geo-crowdsourcing services and the data used by campus accessibility maps (which are commonly based on the ADA standards) are used to provide an optimal set of requirements for personalized accessibility map (PAM). PAM is discussed and analysed in detail, a prototype PAM developed for the University of Pittsburgh is described, and challenges and future work are highlighted. © 2014 Taylor & Francis

Design considerations for a personalized wheelchair navigation system

Individuals with mobility impairments such as wheelchair users are often at a disadvantage when traveling to a new place, as their mobility can be easily affected by environmental barriers, and as such, even short trips can be difficult and perhaps impossible. We envision a personalized wheelchair navigation system based on a PDA equipped with wireless Internet access and GPS that can provide adaptive navigation support to wheelchair users in any geographic environment. Requirements, architectures and components of such a system are described in this paper. © 2007 IEEE

Supplementary Material for: Positional Vomiting as the Initial Manifestation of Bruns Syndrome due to Cysticercosis in the Fourth Ventricle: A Symptom Reminiscent of an Old Disease

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Extraction of Spatio-Temporal Data for Social Networks

Abstract. It is often possible to better understand group change over time through examining social network data in a spatial and temporal context. Providing that context from a text analysis perspective requires identifying locations and associating them with people. This paper presents our GeoRef algorithm to automatically do this person-to-place mapping. It involves the identification of location, and uses syntactic proximity of words in the text to link location to person’s name. We describe an application using the algorithm based upon a small set of data from the Sudan Tribune divided into three periods in 2006 for the Darfur crisis. Contributions of this paper are (1) techniques to mine for location from text (2) techniques to mine for social network edges (associations between location and person), (3) use of the mined data to make spatio-temporal maps, and (4) use of the mined data to perform social network analysis