Comments for S4 GIS Institute http://www.brown.edu/Departments/Spatial_Structures_in_the_Social_Sciences/institute GIS for Social Science Research Thu, 11 Jun 2009 04:05:28 -0400 http://wordpress.org/?v=2.8.5 hourly 1 Comment on Submit Project Abstracts by Jie Yang http://www.brown.edu/Departments/Spatial_Structures_in_the_Social_Sciences/institute/2009/06/submit-project-abstracts/comment-page-1/#comment-13 Jie Yang Thu, 11 Jun 2009 04:05:28 +0000 http://www.brown.edu/Departments/Spatial_Structures_in_the_Social_Sciences/institute/2009/06/submit-project-abstracts/#comment-13 Geographic Access to Hospital Care Access to health care is always a global issue. Access describes people’s ability to use health services when and where they are needed. One of the main goals for health care facility planning is to achieve the equitable geographic distribution of health care resources. In the United States, health care access problems for vulnerable populations such as ethnic minorities, poor groups are attracting increased attention. In addition, for medical conditions that require regular contact with service providers, travel time and distance can create barriers to effective service use. GIS provides a set of tools for describing and understanding the spatial organization of health care, and for exploring how the delivery of health care can be improved. In my study, I will mainly focus on two research questions: 1. Are all communities in Rhode Island within a reasonable distance to a hospital? If not, what are the communities that are farther away from a hospital? 2. Some diseases can be categorized as ambulatory care sensitive conditions (ACSC), which are medical problems that can be treated and managed outside the hospital. Since the admission of such kind of diseases in a hospital shows that a problem in primary care; does the admission rate look high in certain areas? ESRI ArcGIS is used to construct aggregated drive time service area for 11 Rhode Island general hospitals based on 10, 20, and 30 minutes of driving time. ArcView is used to compare the demographic information including population, age, per capita income and unemployment rate in these areas. Hospital admission rates of ACSC are marked at the census tract level using a hospitalization dataset from Rhode Island Department of Health. Geographic Access to Hospital Care
Access to health care is always a global issue. Access describes people’s ability to use health services when and where they are needed. One of the main goals for health care facility planning is to achieve the equitable geographic distribution of health care resources. In the United States, health care access problems for vulnerable populations such as ethnic minorities, poor groups are attracting increased attention. In addition, for medical conditions that require regular contact with service providers, travel time and distance can create barriers to effective service use. GIS provides a set of tools for describing and understanding the spatial organization of health care, and for exploring how the delivery of health care can be improved.
In my study, I will mainly focus on two research questions: 1. Are all communities in Rhode Island within a reasonable distance to a hospital? If not, what are the communities that are farther away from a hospital? 2. Some diseases can be categorized as ambulatory care sensitive conditions (ACSC), which are medical problems that can be treated and managed outside the hospital. Since the admission of such kind of diseases in a hospital shows that a problem in primary care; does the admission rate look high in certain areas?
ESRI ArcGIS is used to construct aggregated drive time service area for 11 Rhode Island general hospitals based on 10, 20, and 30 minutes of driving time. ArcView is used to compare the demographic information including population, age, per capita income and unemployment rate in these areas. Hospital admission rates of ACSC are marked at the census tract level using a hospitalization dataset from Rhode Island Department of Health.

]]> Comment on Submit Project Abstracts by Jiachen Zhou http://www.brown.edu/Departments/Spatial_Structures_in_the_Social_Sciences/institute/2009/06/submit-project-abstracts/comment-page-1/#comment-12 Jiachen Zhou Thu, 11 Jun 2009 00:54:07 +0000 http://www.brown.edu/Departments/Spatial_Structures_in_the_Social_Sciences/institute/2009/06/submit-project-abstracts/#comment-12 Spatial clustering of overweight and obesity in rural South Africa Overweight and obesity are prevalent among South Africans. The South Africa Demographic and Health Survey, a national cross-sectional study in 1998, found that 29.2% of adult men and 56.6% adult women were overweight or obese. In a large population-based survey of body mass and blood pressure in rural KwaZulu/Natal, South Africa in 2003-2004, 58% adults were overweight or obese. Overweight and obesity have been found associated with various cardiovascular diseases, so there is a clear rationale to plan programs of health promotion and primary prevention of obesity to reduce the incidence of cardiovascular disease in South Africa. Although genetic factors have been found to relate to overweight and obesity, it is unlikely that addressing genetic factors alone will be sufficient to fully prevent and manage the epidemic. Environmental factors, including the sociocultural and physical environment, promote increased caloric intake and decreased energy expenditure and underlie much of the high prevalence of overweight and obesity. The prevention and management of overweight and obesity obviously needs a blending of medical expertise and insight into an array of community and environmental factors. In this study, I plan to use spatial analysis techniques to explore whether people with high BMI are living near other people with high BMI, which will indicate the existence of potential environmental predictors of overweight and obesity. Local Moran’s I will be used to measure the spatial autocorrelation. Spatial clustering of overweight and obesity in rural South Africa

Overweight and obesity are prevalent among South Africans. The South Africa Demographic and Health Survey, a national cross-sectional study in 1998, found that 29.2% of adult men and 56.6% adult women were overweight or obese. In a large population-based survey of body mass and blood pressure in rural KwaZulu/Natal, South Africa in 2003-2004, 58% adults were overweight or obese. Overweight and obesity have been found associated with various cardiovascular diseases, so there is a clear rationale to plan programs of health promotion and primary prevention of obesity to reduce the incidence of cardiovascular disease in South Africa. Although genetic factors have been found to relate to overweight and obesity, it is unlikely that addressing genetic factors alone will be sufficient to fully prevent and manage the epidemic. Environmental factors, including the sociocultural and physical environment, promote increased caloric intake and decreased energy expenditure and underlie much of the high prevalence of overweight and obesity. The prevention and management of overweight and obesity obviously needs a blending of medical expertise and insight into an array of community and environmental factors.
In this study, I plan to use spatial analysis techniques to explore whether people with high BMI are living near other people with high BMI, which will indicate the existence of potential environmental predictors of overweight and obesity. Local Moran’s I will be used to measure the spatial autocorrelation.

]]> Comment on Submit Project Abstracts by Trina and Pete http://www.brown.edu/Departments/Spatial_Structures_in_the_Social_Sciences/institute/2009/06/submit-project-abstracts/comment-page-1/#comment-11 Trina and Pete Wed, 10 Jun 2009 21:12:14 +0000 http://www.brown.edu/Departments/Spatial_Structures_in_the_Social_Sciences/institute/2009/06/submit-project-abstracts/#comment-11 Spatial Patterns of Land Use in the Brazilian Amazon, 1990-2007 We use municipio level data from the Brazilian agricultural census and decennial population census to examine the spatial relationship between transportation networks, migration, and land use in the Amazonian states of Pará and Mata Grosso. Our initial analysis looks at land use changes related to soy and rice production between 1990 and 2007, as the production of soy quintupled during this period and rice production doubled. We find the north to south movement of soy, with soy production intensifying along the Cuiba Santarém Highway in Mata Grosso. By the end of the study period, we begin to see the production soy in a few municipios in Pará, both along the major highways in the state and near the new port in Santarém. The intensification of soy production parallels the creation of Brazilian government programs that provide incentives for the production of soy for export. We have not yet mapped the changing spatial distribution of rice production, but expect to observe a different story spatially over time. We hope to complete two animated maps to illustrate the annual change in the percentage of land in production for soy and rice. We also look at the spatial variation in migration using retrospective data from the 2000 decennial census and find clustering by region and along transportation networks. Spatial Patterns of Land Use in the Brazilian Amazon, 1990-2007

We use municipio level data from the Brazilian agricultural census and decennial population census to examine the spatial relationship between transportation networks, migration, and land use in the Amazonian states of Pará and Mata Grosso. Our initial analysis looks at land use changes related to soy and rice production between 1990 and 2007, as the production of soy quintupled during this period and rice production doubled. We find the north to south movement of soy, with soy production intensifying along the Cuiba Santarém Highway in Mata Grosso. By the end of the study period, we begin to see the production soy in a few municipios in Pará, both along the major highways in the state and near the new port in Santarém. The intensification of soy production parallels the creation of Brazilian government programs that provide incentives for the production of soy for export. We have not yet mapped the changing spatial distribution of rice production, but expect to observe a different story spatially over time. We hope to complete two animated maps to illustrate the annual change in the percentage of land in production for soy and rice. We also look at the spatial variation in migration using retrospective data from the 2000 decennial census and find clustering by region and along transportation networks.

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