The Special Nature of Spatial Data

Haining, Robert

doi:10.4135/9780857020130.n2

Cited by 24 publications

(9 citation statements)

References 38 publications

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“…3). However, we are aware of the limitations of the visual interpretation of ratios through maps due to standard errors and differences in area size [53], and we will leave this evaluation of clustering by calculating Global Moran's I in the next section.…”

Section: Methods and Analysismentioning

confidence: 99%

Observing regional divergence of Chinese nanotechnology centers

Motoyama¹,

Cao²,

Appelbaum³

2014

Technological Forecasting and Social Change

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Section: Methods and Analysismentioning

confidence: 99%

Observing regional divergence of Chinese nanotechnology centers

Motoyama¹,

Cao²,

Appelbaum³

2014

Technological Forecasting and Social Change

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“…From a methodological point of view, nearly all of the very few happiness and well-being studies that incorporated space in the analysis did so with the use of single-level econometric models that typically include a dummy variable for area (e.g., region, country, or state). It can be argued that although such modeling approaches have been very successful in adding a geographical dimension to happiness research, there are a number of conceptual and methodological issues pertaining to the role of space (Anselin and Griffith 1988; Haining 2009) as well as the nested structure of population survey microdata that need to be considered. More specifically, individuals are clustered within households, which in turn are clustered in houses, neighborhoods, cities, conurbations, and regions.…”

Section: Measuring Subjective Happiness and Its Determinants: A Multilevel Problem?mentioning

confidence: 99%

Happy People or Happy Places? A Multilevel Modeling Approach to the Analysis of Happiness and Well-Being

Ballas

Tranmer

2011

International Regional Science Review

171

106

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This article aims to add a regional science perspective and a geographical dimension to our understanding of substantive questions regarding self-reported happiness and well-being through the specification and use of multilevel models. Multilevel models are used with data from the British Household Panel Survey and the Census of UK population to assess the nature and extent of variations in happiness and well-being to determine the relative importance of the area (district, region), household, and individual characteristics on these outcomes. Having taken into account the characteristics at these different levels, we are able to determine whether any areas are associated with especially positive or negative feelings of happiness and well-being. Whilst we find that most of the variation in happiness and well-being is attributable to the individual level, some variation in these measures is also found at the household and area levels, especially for the measure of well-being, before we control for the full set of individual, household, and area characteristics. However, once we control for these characteristics, the variation in happiness and well-being is not found to be statistically significant between areas.

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“…The number of geographic units being used in this study has a direct impact on our results and analysis. This issue is related to the modifiable areal unit problem (MAUP) where the results (i.e., interpretation of statistical analysis and cartographic visualizations) obtained from analysing the aggregated data are dependent on the particular scale and boundaries used for the spatial analysis [76]. In addition to the LHIN regions, public health in the Southern Ontario region is also governed by Public Health Ontario who operates within 29 administrative boundaries (i.e., an increased number of geographic units compared to the LHINs) [77].…”

Section: Discussionmentioning

confidence: 99%

Bayesian Space–Time Analysis of Brain Cancer Incidence in Southern Ontario, Canada: 2010–2013

Persad¹

2019

Medical Sciences

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Canada has one of the highest incidence rates of brain cancer in the world. This study investigates the space–time variation of brain cancer risk across Southern Ontario, Canada. A Bayesian spatio-temporal regression model is used to estimate the relative risk of brain cancer in the 12 spatial health units of Southern Ontario over a four-year period (2010–2013). This work also explores the association between brain cancer and two potential risk factors: traumatic head injury (THI) and excess body fat (EBF). Across all areal units from 2010–2013, results show that the relative risk of brain cancer ranged from 0.83 (95% credible interval (CI) 0.74–0.91) to 1.26 (95% CI 1.13–1.41). Over the years, the eastern and western health units had persistently higher risk levels compared to those in the central areas. Results suggest that areas with elevated THI rates and EBF levels were also potentially associated with higher brain cancer relative risk. Findings revealed that the mean temporal trend for cancer risk progression in the region smoothly decreased over time. Overall, 50% of the health units displayed area-specific trends which were higher than the region’s average, thus indicating a slower decrease in cancer rates for these areas in comparison to the mean trend.

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The Special Nature of Spatial Data

Cited by 24 publications

References 38 publications

Observing regional divergence of Chinese nanotechnology centers

Observing regional divergence of Chinese nanotechnology centers

Happy People or Happy Places? A Multilevel Modeling Approach to the Analysis of Happiness and Well-Being

Bayesian Space–Time Analysis of Brain Cancer Incidence in Southern Ontario, Canada: 2010–2013

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