Different Levels Of Geography Research Articles

Social Statistics Transformation: Understanding the population through the production of income by ethnicity statistics from administrative data

ObjectivesThe lack of an income question on the Census has meant the production of multivariate income by ethnicity statistics has not been possible in census outputs to date. Our ambition is to provide individual-level records for every member of the usually resident population of England and Wales using admin data.
 MethodExciting progress has been made in the development of admin-based characteristics measures, including the ongoing feasibility research to produce admin-based datasets on ethnic group and income. Demonstrated improved results include the coverage of the population steadily increasing and methods to create these datasets gradually improving.
 Access to these record-level administrative datasets has allowed us to combine admin-based income and ethnicity measures developed in previous research, linking individuals between the two. We review the coverage of the combined dataset and the feasibility of producing multivariate statistics at subnational levels in England and Wales for the first time.
 ResultsThis presentation will showcase our innovative progress so far. By combining admin-based income and admin-based ethnicity datasets, we established an income and a stated ethnicity for 77.1% of people in England and 82.1% of people in Wales aged 16 years and over in the admin-based Statistical Population Dataset (our population base).
 For the first time, we have produced income percentiles for ethnic groups at different levels of geography in England and Wales including national figures, regional figures and figures for local authorities and lower layer super output areas; although statistical disclosure control means that some of the figures have been suppressed.
 We will highlight some of the challenges in using administrative data sources to produce these statistics and in assessing their statistical quality.
 ConclusionOur research developing these statistics is truly novel and shows much promise. Future work will include research to improve the univariate admin-based measures that are used, to continue to explore the limitations of the combined dataset, to explore the data by occupied address, and explore methods to adjust for missingness.

Recent trends in the spatial distribution of human capital: Are skill levels converging across regions in England and Wales?

AbstractIn modern knowledge‐based societies human capital is the single most important determinant of regional inequalities in productivity and standards of living. Using a newly constructed data set that allows the analysis of educational attainments at different levels of geography, this paper evaluates spatial inequalities and the degree of convergence in the distribution of human capital across areas in England and Wales during the second decade of the 21st century. Our results show this was a period characterised by a large increase in educational attainment and skill intensity. However, the growth in skill intensity was far from uniform across space. In particular, we find strong evidence of both absolute σ‐divergence and β‐convergence in the distribution of skills. Thus, even if low‐skill areas grew on average more than other areas with higher skill intensity at the start of the period, the stochastic dominance analyses provide strong evidence of an unambiguous increase in absolute inequalities so that by end of the decade the skill gap between low‐ and high‐skill areas had significantly widened. We present new spatial and aspatial evidence that sheds light on those inequalities and the changes in the spatial configuration of human capital over the last decade. Despite the implementation of policies aimed at reducing regional inequalities, many low skill areas struggled to attract talent so that the gap with most skilled areas widened over that period likely contributing to the persistence of the well‐documented large spatial economic inequalities in this country.

Harmonising Incompatible Datasets to Enable GIS Use to Study Non-communicable Diseases in Tonga

We investigated the use of geographic information systems (GIS) to study the prevalence of non-communicable diseases (NCDs) in association with area deprivation within Tongatapu, the largest island in Tonga. This is a case study to determine what is possible to achieve since various data issues influenced by institutional and organizational barriers exist, affecting data integration and GIS use. A data conversion framework was developed using geographic conversion tables (GCTs) to process existing data into a compatible format and create new geographies to analyse the prevalence of NCDs and area deprivation across different levels of geography. Area deprivation was higher in the rural district and was associated with the prevalence of NCDs. However, at lower levels of geography, the distribution and patterns of NCDs, and area deprivation were unclear. This was influenced by the methods of data collection, recording and dissemination. There is a need for a national action plan outlining the standard operating procedures for all stakeholders to adhere to, and thereby produce and disseminate comprehensive, reliable, and high-quality data. Otherwise, data will be collected for basic reporting but impractical for sophisticated analysis and research. We suggest an investigation into dasymetric mapping to disaggregate population data and develop automating processes for large national datasets.

Relative deprivation: a new derivation and application

ABSTRACT We derive and express the Yitzhaki index of relative deprivation as a function of mean and median household income within a reference group and one’s income, under the assumption that individual income follows the log-normal distribution or the Pareto distribution. We also suggest a way to calculate annual US regional mean and median household income in each reference group using aggregate income and household size data. Therefore, unlike prior literature that utilizes Decennial Census data due to its big sample size, our suggested methodology allows annual calculation of relative deprivation at different levels of geography (state or county). In an application of the derived index presented to illustrate its feasibility, we analyse the relationship between relative deprivation and mental health, finding suggestive evidence that increases in relative deprivation negatively affect mental health in the United States.

New Evidence on the Relationship between Inequality and Health

The relative income hypothesis suggests that an individual’s health is impacted by the income of others. However, prior studies suffer from mixed empirical findings that could be due to a lack of annual individual income data with sufficient sample size. We apply a new methodology to calculate a variety of income inequality measures based on aggregate income and household size data from various Federal data sources. Our proposed methodology provides a way to express various income inequality measures as a function of the ratio of mean to median household income under the assumption that individual income is log-Normally distributed. This approach produces a variety of precise annual income inequality measures at different levels of geography, thus solving the sample size problem by incorporating externally calculated inequality measures. Combining the 2001-2012 editions of the U.S. Behavioral Risk Factor Surveillance System with annual regional income inequality measures derived from our methodology enables us to estimate both the contemporaneous and the lagged effect of income inequality on individual health outcomes. In general, we find statistically significant evidence supporting the income inequality hypothesis and the relative deprivation hypothesis, which suggests that greater income inequality adversely affects health status in the United States.

A multi-level modeling approach to understanding residential segregation in the United States

A well-known limitation of commonly used segregation measures is their inability to describe patterns at multiple scales. Multi-level modeling approaches can describe how different levels of geography contribute to segregation, but may be difficult to interpret for non-technical audiences and have rarely been applied in the US context. This paper provides a readily interpretable description of multi-scale Black–non-Black segregation in the United States using a multi-level modeling approach and the most recent Census data available. We fit a three-level random intercept multi-level logistic regression model predicting the proportion of the population that is Black (Hispanic and non-Hispanic) at the block group level, with block groups nested in tracts and tracts nested in Metropolitan Statistical Areas (MSAs). For the 102 largest MSAs in the United States, we then estimated the extent to which micro- versus meso-level variability drives overall racial residential patterning within the MSA. Finally, we created a typology of racial residential patterning within MSAs based on the total proportion of the MSA population that is Black and the relative contribution of block groups (micro) versus tracts (meso) in driving variation. We find that nearly 80% of the national variation in the geographic concentration of Black residents is driven by within-MSA, tract-level processes. However, the relative contribution of small versus larger scales to within-MSA segregation varies substantially across metropolitan areas. We detect five meaningfully different types of metropolitan segregation across the largest MSAs. Multi-level descriptions of segregation may help planners and policymakers understand how and why segregated residential patterns are evolving in different places and could provide important insights into interventions that could improve integration at multiple scales.

Where the Action is in Crime? An Examination of Variability of Crime Across Different Spatial Units in The Hague, 2001–2009

To identify how much of the variability of crime in a city can be attributed to micro (street segment), meso (neighborhood), and macro (district) levels of geography. We define the extent to which different levels of geography are important in understanding the crime problem within cities and how those relationships change over time. Data are police recorded crime events for the period 2001–2009. More than 400,000 crime events are geocoded to about 15,000 street segments, nested within 114 neighborhoods, in turn nested within 44 districts. Lorenz curves and Gini coefficients are used to describe the crime concentration at the three spatial levels. Linear mixed models with random slopes of time are used to estimate the variance attributed to each level. About 58–69 % of the variability of crime can be attributed to street segments, with most of the remaining variability at the district level. Our findings suggest that micro geographic units are key to understanding the crime problem and that the neighborhood does not add significantly beyond what is learned at the micro and macro levels. While the total number of crime events declines over time, the importance of street segments increases over time. Our findings suggest that micro geographic units are key to understanding the variability of crime within cities—despite the fact that they have received little criminological focus so far. Moreover, our results raise a strong challenge to recent focus on such meso geographic units as census block groups.

UK open source crime data: accuracy and possibilities for research

In the United Kingdom, since 2011 data regarding individual police recorded crimes have been made openly available to the public via the police.uk website. To protect the location privacy of victims these data are obfuscated using geomasking techniques to reduce their spatial accuracy. This paper examines the spatial accuracy of the police.uk data to determine at what level(s) of spatial resolution – if any – it is suitable for analysis in the context of theory testing and falsification, evaluation research, or crime analysis. Police.uk data are compared to police recorded data for one large metropolitan Police Force and spatial accuracy is quantified for four different levels of geography across five crime types. Hypotheses regarding systematic errors are tested using appropriate statistical approaches, including methods of maximum likelihood. Finally, a “best-fit” statistical model is presented to explain the error as well as to develop a model that can correct it. The implications of the findings for researchers using the police.uk data for spatial analysis are discussed.

The Rural Economic Capacity Index (RECI)

Economic development practitioners and theorists recognize that community-based strategies offer the best opportunity for rural economic development. But, rural communities also need assistance in identifying and implementing their strategies. Two key needs are financial support and technical assistance. In this article, the authors describe a benchmarking tool that provides rural communities in Newfoundland and Labrador, Canada with a means to identify their individual strengths and weaknesses, and how they compare with immediate peers. This is the typical benchmarking function, but the tool also provides a way to show individual communities the benefits of regional agglomerations at different levels of geography. Unlike most benchmarking exercises that simply provide lists of indicators, the authors link the indicators through a simplified structural model of the local economy. By focusing on composite indicators, the Rural Economic Capacity Index provides community leaders with a sense of the “big picture” that can help them in the process of framing a development strategy.

Inequality shaping processes and gated communities in US western metropolitan areas.

This paper investigates the social dimensions of gated communities (GCs) in US western metropolitan areas and how they contribute to segregation. We use geographically referenced data of GCs, and introduce a local metric based on social distance indices (SDI). This multivariate spatial analysis investigates homogeneity in three aspects: race and ethnicity, economic class and age between 2000 and 2010 census. The results indicate contrasting effects given different levels of geography. GCs significantly contribute to segregation patterns at a local level, and this has been locally reinforcing. Although socioeconomic segregation and ethnic status yield the most prevalent structure of local distance, gated enclaves are also significantly structured by age. The findings are considered in the context of a metropolitan decline in levels of segregation. Data also show that GCs are likely to be located within large racially homogeneous areas, and do not significantly contribute to racial segregation.

Do alternative spatial healthcare access measures tell the same story?

To effectively target area and population with relatively poorer healthcare access requires information on how access to healthcare varies spatially. Considering hospitals as important components of the healthcare system, this case study of Central Missouri presents a comparison of the relatively underserved areas and population based on the results from three alternative indicators of spatial hospital access calculated at three different levels of geography. Results indicate that the alternative spatial healthcare access indicators show considerably different pictures of the relative ranking of census units, and identification of the relatively underserved areas and population. The relative ranking of census units and the identification of relatively underserved area and population are all critical indicators for policy makers when public resources are limited and distributive choices must be made.

Co-variation in dimensions of smoking behaviour: A multivariate analysis of individuals and communities in Canada

We evaluated the effects of socioeconomic status on the prevalence of current smoking, number of cigarettes smoked per day and pack-years, and the extent to which prevalence and consumption co-vary across communities, health regions, and provinces in Canada between 2001 and 2010. Current smoking, cigarettes per day, and pack-years were considered as outcomes within individuals using a multilevel analytical framework. Markers of SES were education, income, and occupation. Residual covariance estimated at the different levels of geography was used to determine if areas high in current smoking were also high on levels of consumption. A strong inverse gradient was found between education and current smoking and level of consumption with large variation found in levels of consumption between individual smokers. The co-variation between current smoking and level of consumption was positive and statistically significant at the level of communities and health regions. Our findings suggest that novel policy efforts may be needed to encourage smoking prevention/cessation among certain population groups and in places with high levels of smoking prevalence and tobacco use intensity.

Testing geographic and economic distance of agglomeration economies

PurposeThe purpose of this paper is to test geographic and economic distance of industrial agglomeration.Design/methodology/approachBased on a recent census database in China, we estimate the production function of Chinese firms, focusing on the impact of agglomeration economies.FindingsThe estimation results provide strong evidence that agglomeration effects decline with increasing geographic and economic distance.Originality/valuePrevious studies examine agglomeration effects at certain geographic and industrial level, but largely ignore that agglomeration benefit may be different at different levels of geography and industry. This paper contributes to the literature by examining the geographic and economic distance of agglomeration economies, and shows a clear pattern on geographic and industrial scope of agglomeration economies.

Crowd out effects of place-based subsidized rental housing: New evidence from the LIHTC program

Since its inception in 1987, the Low Income Housing Tax Credit (LIHTC) program has ballooned into the largest ever source of subsidized construction of low-income housing in the United States, accounting for one-third of all recent multi-family rental construction. This paper examines the crowd out effects of this increasingly important source of low-moderate income housing. To do so, we analyze the impact of LIHTC construction at three different levels of geography, MSA, county, and 10-mile radius circles. This allows us to employ increasingly extensive geographic fixed effects that help to difference away unobserved factors. Political variables are also used as instruments to further facilitate identification. In all of our models, IV estimates yield substantially greater crowd out than OLS, confirming the endogenous attraction of LIHTC development to areas ripe for new construction. Our most robust IV estimates indicate that nearly 100% of LIHTC development is offset by a reduction in the number of newly built unsubsidized rental units, although the confidence band around this point estimate allows for less dramatic assessments. Additional estimates suggest that LIHTC development has a much more moderate impact on construction of owner-occupied housing, but these estimates are imprecise. Overall, while LIHTC development may well affect the location of low-moderate income rental housing opportunities, our estimates suggest that the impact of the program on the number of newly developed rental housing units appears to be small.

Intercensal denominators - feasibility of using the Labour Force Survey to estimate mortality rates by NS-SEC

The purpose of this analysis is to investigate the feasibility of using the Labour Force Survey (LFS) to provide population denominators for the estimation of mortality rates by the National Statistics Socio-economic Classification (NS-SEC). This is in order to contribute to the monitoring of health inequalities over time. Currently it is possible to produce mortality rates only at the time of a census when populations are enumerated by occupation and NS-SEC. The Labour Force Survey (LFS) was used to provide population denominators by age, sex and NS-SEC for various time periods. Numerators for mortality rates were derived by obtaining aggregate deaths by age group and NS-SEC analytic class for men aged 25-64. Standardised mortality rates were then derived. Results for 2001-03 were compared with published estimates which used 2001 Census-based denominators. Significant differences were detected for most NS-SEC classes between mortality rate estimates, based on the 2001 Census, and those based on the contemporary LFS dataset. However the results for different years, based on LFS denominators, suggest that a series of mortality rates using LFS-based denominators appeared to have internal consistency. The LFS-based estimates showed a statistically significant decrease in mortality rates between the periods 2001-03 and 2005-07, across all but one NS-SEC classes. The precision of LFS-based estimates at different levels of geography was also assessed in order to determine the feasibility of using these estimates to monitor inequalities at sub-national level. The LFS can be used to produce regular population denominators for the estimation of mortality rates, to assess health inequalities by NS-SEC for men at the level of England & Wales, both on an annual basis and over three-year time periods. It is recommended that, in order for the LFS-based estimates to be effective over time, they would have to be related to each other rather than to the census-based ones. At the level of Government Office Region (GOR), it would also be possible to do this, but on a three-year period basis only. Below this level of geography, estimates would be insufficiently precise. Information exists on the LFS to produce a similar analysis for women.

Creating a Geospatial Database of Risks and Resources to Explore Urban Adolescent Substance Use

This article illustrates the methodology of creating a comprehensive geospatial database in order to systematically understand the social ecology of risk and protection for urban youth. The challenges and future opportunities involved with this complex work were reviewed, and specific examples were provided to guide researchers. Data were collected from a Washington, DC adolescent substance abuse treatment sample to construct a geospatial database to evaluate urban youths' social environmental risk and resources. A geographic information systems (GIS) approach was adopted to integrate a large array of variables at different levels of geography. For example, risk factors included proximity to crime hotspots, and other known potential establishments with negative influence (such as liquor stores). We also used GIS to assess the subjects' accessibility to protective resources such as public libraries, recreational, parks, and police stations. Unique to our method was the collecting and mapping of each teen's activity locations (places they typically frequent). These data form “risk and protection exposure” estimates for each teen. Finally, we illustrated the specific methods for creating a dynamic geospatial database for urban youth and present future analytical approaches and challenges with these type of data.