Small-Area Population Estimation: an Integration of Demographic and Geographic Techniques

Knowledge of detailed and accurate population information is essential to analyze and address a wide variety of socio-economic, political, and environmental issues and to support necessary planning practices for both public agencies and the private sector. However, such important data are generally only available once every decade through the National Census. Moreover, populations in some rapidly-developing areas may increase quickly, such that this ten-year frequency does not meet the needs of these areas. Therefore, a cost-effective method for population estimation is necessary. To address this issue, this research integrated geographic, sociological, and demographic theories and exploited remotely sensed imagery and geographic information system (GIS) datasets to derive better population estimates at the census block level, the finest level of the national census. Specifically, three new approaches have been proposed in this dissertation to assist in the improvement of small-area population estimation accuracy. First, existing remotely sensed and GIS data have been adopted to estimate two major components of a demographic framework, including the redistribution of newly built dwelling units from the aggregated geographic level to the census block level and the estimation of persons per household (PPH) at such a fine scale. Second, in addition to the use of existing data, new urban environmental indicators were also extracted and employed to improve population estimation. In particular, to implement the automatic enumeration for individual housing units, a new spectral index, biophysical composition index (BCI), has been proposed to derive impervious surface information, a desirable urban environmental parameter. Third, using the extracted high-resolution urban environmental information and GIS data, a new bottom-up method was developed for small-area population estimation at the census block level by incorporating these high-resolution data into the demographic framework. Analyses of the results suggest three major conclusions. First, existing GIS spatial factors, together with demographic information, can assist in improving the accuracy of small-area population estimation. Second, the BCI has a closer relationship with impervious surface area than do other popular indices. Moreover, it was shown to be the most effective index of the four evaluated for separating impervious surfaces and bare soil, which consequently might assist in more accurately deriving fractional land cover values. Third, the use of the new environmental indicators extracted from remote sensing imagery and GIS data and the integration of demographic and geographic approaches has significantly improved the estimation accuracy of housing unit (HU) numbers, PPH, and population counts at the census block level. Therefore, this research contributes to both the remote sensing and applied demography fields. The contribution to the remote sensing field lies in the development of a novel spectral index to characterize urban land for monitoring and analyzing urban environments. This index provided more significant separability between impervious surfaces and bare soil than did other existing indices. Moreover, three major contributions have been made in the field of applied demography: 1) the generation of accurate HU estimates using high-resolution remote sensing and GIS datasets, 2) the development of a model to derive an accurate PPH estimate, and 3) the improvement of small-area population estimation accuracy through the integration of geographic and demographic approaches

Automating the administration boundary design process using Hierarchical Spatial Reasoning theory and Geographical Information Systems

This paper addresses the problems associated with the integration of data between incongruent boundary systems. Currently, the majority of spatial boundaries are designed in an uncoordinated manner with individual organisations generating individual boundaries to meet individual needs. As a result, current technologies for analysing geospatial information, such as geographic information systems (GISs), are not reaching their full potential. In response to the problem of uncoordinated boundaries, the authors present an algorithm for the hierarchical structuring of administrative boundaries. This algorithm applies hierarchical spatial reasoning (HSR) theory to the automated structuring of polygons. In turn, these structured boundary systems facilitate accurate data integration and analysis whilst meeting the spatial requirements of selected agencies. The algorithm is presented in two parts. The first part outlines previous research undertaken by the authors into the delineation of administrative boundaries in metropolitan regions. The second part outlines the distinctly different constraints required for administrative-boundary design in rural areas. The formalisation of the algorithm has taken place in a GIS environment utilising Avenue, an object-orientated programming language that operates under ArcView, the desktop software developed and distributed by ESRI

Automating the administration boundary design process using Hierarchical Spatial Reasoning theory and Geographical Information Systems

This paper addresses the problems associated with the integration of data between incongruent boundary systems. Currently, the majority of spatial boundaries are designed in an uncoordinated manner with individual organisations generating individual boundaries to meet individual needs. As a result, current technologies for analysing geospatial information, such as geographic information systems (GISs), are not reaching their full potential. In response to the problem of uncoordinated boundaries, the authors present an algorithm for the hierarchical structuring of administrative boundaries. This algorithm applies hierarchical spatial reasoning (HSR) theory to the automated structuring of polygons. In turn, these structured boundary systems facilitate accurate data integration and analysis whilst meeting the spatial requirements of selected agencies. The algorithm is presented in two parts. The first part outlines previous research undertaken by the authors into the delineation of administrative boundaries in metropolitan regions. The second part outlines the distinctly different constraints required for administrative-boundary design in rural areas. The formalisation of the algorithm has taken place in a GIS environment utilising Avenue, an object-orientated programming language that operates under ArcView, the desktop software developed and distributed by ESRI

Bringing Zoning into Focus: A Fine-Grained Analysis of Zonings Relationships to Housing Affordability, Income Distributions, and Segregation in Connecticut

For more than a century, local governments throughout the United States have used zoning to shape future growth. Through rules that regulate what sorts of housing can be built where, localities may either allow for new development or restrict construction to maintain the status quo. Using a first-of-its-kind database of zoning laws across an entire state, we provide evidence that strict zoning regulations limiting construction to single-family homes are associated with inadequate access to affordable housing and with the segregation of people by income, race, and ethnicity.We leverage the Connecticut Zoning Atlas, a unique dataset of zoning texts tied to geospatial files that allow for georeferenced comparisons between the zoning laws adopted by 180 localities. We overlay the geographies of zoning districts on spatially differentiated demographic and economic indicators at the neighborhood level. Analyzing and comparing this information allows us to provide a comprehensive view of the relationships between zoning laws, property values, and residents' demographic and economic characteristics.Our research focuses on a state with stark disparities in residential land uses: only 2 percent of Connecticut's land is zoned to allow the by-right construction of multifamily buildings with three or more units per parcel, while 91 percent of its land allows only the construction of single-family housing by right. Our analysis reveals that suburbs and towns have the most restrictive zoning rules by several measures, while the largest cities more readily permit multifamily construction. We show that the residents of neighborhoods with mostly single-family zoning, on average, have significantly higher household incomes and are much more likely to be white, much less likely to be Black or Hispanic, more likely to have a bachelor's degree, and much more likely to own their homes than residents of neighborhoods where zoning allows for multifamily building construction. These findings paint a picture of a state where localities' zoning either divides or reinforces the division of residents by income, race, ethnicity, and education levels. We also apply a segregation index to explore the associations between zoning rules and neighborhood- and locality-level segregation. After controlling for other characteristics, we find that higher concentrations of high-income and white residents are associated with lower number-of-unit zoning policies. We also find that higher concentrations of low-income, Black, Hispanic, and other residents of color are associated with zoning allowing the construction of two or more housing units per parcel and higher shares of renter-occupied housing.Together, these findings bring new insight into the relationship between zoning policy and residents' geographic distribution. Our results clearly point to the links between zoning laws, rental housing availability, and inequitable distributions of populations within and across jurisdictions. Policymakers considering how to improve access to opportunity while reducing income or racial segregation should evaluate the potential for altering local zoning codes to allow greater diversity of housing construction and tenure types in more places

Using Geographic Information Systems to measure retail food environments: discussion of methodological considerations and a proposed reporting checklist (Geo-FERN)

Geographic Information Systems (GIS) are widely used to measure retail food environments. However the methods used are hetrogeneous, limiting collation and interpretation of evidence. This problem is amplified by unclear and incomplete reporting of methods. This discussion (i) identifies common dimensions of methodological diversity across GIS-based food environment research (data sources, data extraction methods, food outlet construct definitions, geocoding methods, and access metrics), (ii) reviews the impact of different methodological choices, and (iii) highlights areas where reporting is insufficient. On the basis of this discussion, the Geo-FERN reporting checklist is proposed to support methodological reporting and interpretation

Urban sprawl in the state of Missouri : current trends, driving forces, and predicted growth on Missouri's natural landscape

Title from PDF of title page (University of Missouri--Columbia, viewed on March 5, 2013).The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file.Dissertation advisor: Dr. Hong S. HeIncludes bibliographical references.Vita.Ph.D. University of Missouri--Columbia 2012."December 2012"Missouri reflects a full range of sprawl characteristics that include large metropolitan centers, which led growth in 1980s, and smaller metropolitan and rural areas, which led growth in 1990s. In order to study the historical patterns of sprawl, there is a need to quantitatively and geographically depict the extent and density of impervious surface for three time periods of 1980, 1990, and 2000 for the entire state of Missouri. Mapped impervious surface is the best candidate of ancillary data for dasymetric mapping of population in several comparison studies. The current research examines the performances of dasymetric mapping of population with imperviousness as ancillary data and regression analysis of population using imperviousness as a predictor Results from this work can be aggregated to any geographical unit (hydrologic boundaries, administrative boundaries, etc.). A pilot future urban growth study for the two decades of 1980s and 1990s was done in Missouri. The historical urban growth of the two decades were analyzed then coupled with various predictor variables to investigate the influence of each predictor variables towards the process of urban growth. The knowledge learned from the process is then used to build an urban growth simulation model that is GIS-based with open framework for ease of management and improvement. Pixel level urban growth was simulated for year 2010, 2020 and 2030. This model framework is developed with the ultimate goal of simulating urban growth for the entire state of Missouri.Includes bibliographical reference

A historical GIS for England and Wales: a framework for reconstructing past geographies and analysing long-term change

PhDThis thesis describes the creation and possible uses of a Geographical Information System that contains the changing boundaries of the major administrative units of England and Wales from 1840 to 1974. For over 150 years the census, the General Register Office, and others have used these units to publish a wealth of data concerning the population of the country. The key issue addressed by the thesis is that changes in the administrative geography have hampered much research on long-term change in society that could have been done using these sources. The goal of the thesis is the creation of framework to allow the analysis of long-term socio-economic change that makes maximum use of the available data. This involves not only making use of the data's attribute (statistical) component, but also their spatial and temporal components. In order to do this, the thesis provides solutions to two key problems: the first is how to build a GIS containing administrative units that incorporates an accurate record of their changing boundaries and can be linked to statistical data in a flexible manner. The second is how to remove the impact of boundary changes when comparing datasets published at different dates. This is done by devising a methodology for interpolating data from the administrative units they were published using, onto a single target geography. An evaluation of the accuracy of this interpolation is performed and examples are given of how this type of research could be conducted. Taken together, these will release information locked up within historical socio-economic statistics by allowing space to be explicitly incorporated into any explorations of the data. This, in turn, allows research to explore the past with increased levels of both spatial and attribute data for longer time periods

Trends and concerns in digital cartography

CISRG discussion paper ;