Editorial: Environmental Exposure and Small Area Data

2005 ◽  
Vol 12 (3) ◽  
pp. 257-257
Author(s):  
Andrew B. Lawson
Keyword(s):  
Environmental Exposure ◽  
Small Area ◽  
Area Data

scholarly journals Small-area data on socioeconomic status and immigrant groups for evaluating equity of early cancer detection and care

2021 ◽  
pp. 1-6
Author(s):  
Ulf Strömberg ◽  
Brandon L. Parkes ◽  
Amir Baigi ◽  
Carl Bonander ◽  
Anders Holmén ◽  
...  
Keyword(s):  
Socioeconomic Status ◽  
Cancer Detection ◽  
Small Area ◽  
Early Cancer ◽  
Early Cancer Detection ◽  
Area Data

Use of Small-area Data to Support a Community Agenda in Boston Chinatown

2002 ◽  
Vol 7 (2) ◽  
pp. 203-219
Author(s):  
Doug Brugge ◽  
Martha Tai
Keyword(s):  
Small Area ◽  
Area Data

Inference from ecological models: Estimating the relative risk of stroke from air pollution exposure using small area data

2010 ◽  
Vol 1 (2-3) ◽  
pp. 123-131 ◽  
Author(s):  
Robert Haining ◽  
Guangquan Li ◽  
Ravi Maheswaran ◽  
Marta Blangiardo ◽  
Jane Law ◽  
...  
Keyword(s):  
Air Pollution ◽  
Relative Risk ◽  
Small Area ◽  
Ecological Models ◽  
Air Pollution Exposure ◽  
Pollution Exposure ◽  
Area Data

scholarly journals Geographical inequalities in uptake of NHS funded eye examinations: Poisson modelling of small-area data for Essex, UK

2017 ◽  
Vol 40 (2) ◽  
pp. e171-e179 ◽  
Author(s):  
Darren Shickle ◽  
Tracey M Farragher ◽  
Chris J Davey ◽  
Sarah V Slade ◽  
James Syrett
Keyword(s):  
Small Area ◽  
Geographical Inequalities ◽  
Poisson Modelling ◽  
Area Data

Spatial Linear Regression from Census Microdata: Combining Microdata and Small Area Data

2009 ◽  
Vol 41 (9) ◽  
pp. 2215-2231 ◽  
Author(s):  
Nicholas N Nagle
Keyword(s):  
Social Sciences ◽  
Regression Models ◽  
Small Area ◽  
Statistical Tests ◽  
Statistical Significance ◽  
Geographic Scale ◽  
Density Maps ◽  
Neighborhood Scale ◽  
Source Of Information ◽  
Area Data

Census microdata have become an extremely valuable source of information in social sciences research. These data, however, must have very coarse geographic resolution in order to protect respondent anonymity. Thus the geographic scale of these microdata sources is drastically different from the scale of many spatial processes—particularly neighborhood-scale processes. It is suggested that this difference in geographic scales creates a problem of conclusion validity for regression models which use anonymized microdata: measures of statistical significance are biased in these models. A correction to this problem in which small area data and population-density maps are used to estimate the effects of spatial dependence is presented. Monte Carlo evidence is presented which demonstrates that the conclusion-validity problem may be severe in practice. Further, this evidence shows that the suggested correction with small area data restores conclusion validity to statistical tests.

Statistical Inferences from Small Area Data

Social Forces ◽  
1960 ◽  
Vol 38 (4) ◽  
pp. 341-348
Author(s):  
J. M. Beshers
Keyword(s):  
Small Area ◽  
Statistical Inferences ◽  
Area Data

Determining the Structure of Neighbourhood Cohesion

2012 ◽  
Vol 3 (4) ◽  
pp. 20-42 ◽  
Author(s):  
Kerstin Hermes ◽  
Michael Poulsen
Keyword(s):  
Los Angeles ◽  
Residential Segregation ◽  
Small Area ◽  
Important Determinant ◽  
Economic Status ◽  
Ethnically Diverse ◽  
Healthy Communities ◽  
The Social ◽  
Spatially Distributed ◽  
Area Data

Social cohesion is an important determinant of functioning and healthy communities but its spatial distribution and relation to residential segregation within cities has not been adequately addressed due to the lack of small area data. A disconnect exists between the social capital and segregation literature. This paper presents how neighbourhood cohesion is spatially distributed in Sydney and Los Angeles using synthetic spatial microdata. The results indicate that Sydney has a relatively dense clustering of neighbourhood cohesion, whereas in Los Angeles it is more dispersed. In both cities, cohesion is highest in Anglo/white concentrations, and lowest in ethnically diverse areas. In Los Angeles, neighbourhood cohesion is significantly higher in African American concentrations than in Hispanic and Asian concentrations. Overall cohesion rises with the economic status in Los Angeles but not in Sydney.

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