Smaller Geographic Units Research Articles

Season, weather, and suicide–Further evidence for ecological complexity

Backgroundeasonality in suicide is reported worldwide, and peaks in late spring. Despite the potential connection to the weather, associations between meteorological variables and suicide does not explain seasonality. Studies testing for seasonality while controlling for the weather show patterns that are more complex than a straightforward link between spring-like weather and suicide. MethodsWe tested whether seasonality in suicide was due to meteorological variation (hours of sunshine, rainfall, or temperature) in a novel population (Scotland; 2003–2013). We also sought to further explore the ecological complexity demonstrated in previous work by testing associations at a single location (Tay Road Bridge; 1968–2017). RESULTS: We found peaks in suicidal behavior in June at the bridge, but no seasonality for Scotland as a whole. Seasonality was reduced when we controlled for maximum temperature and hours of sunshine. We found patterns to be dependent upon sex, with stronger seasonal and meteorological effects amongst men. LimitationsOur study was exploratory and relies on population-level data. ConclusionsSeasonal and meteorological effects on suicide are dependent upon local and individual context, with significant effects apparent at the Tay Road Bridge and not across Scotland as a whole. Men may be more sensitive to season and weather. In order to determine whether seasonality in suicide is due to meteorological variation, future research should test patterns in small geographical units, in men and women, and for different suicide methods, and seek to identify the social and physical factors which predict variation in patterns.

Using geographical analysis to identify child health inequality in sub-Saharan Africa.

One challenge to achieving Millennium Development Goals was inequitable access to quality health services. In order to achieve the Sustainable Development Goals, interventions need to reach underserved populations. Analyzing health indicators in small geographic units aids the identification of hotspots where coverage lags behind neighboring areas. The purpose of these analyses is to identify areas of low coverage or high need in order to inform effective resource allocation to reduce child health inequity between and within countries. Using data from The Demographic and Health Survey Program surveys conducted in 27 selected African countries between 2010 and 2014, we computed estimates for six child health indicators for subnational regions. We calculated Global Moran’s I statistics and used Local Indicator of Spatial Association analysis to produce a spatial layer showing spatial associations. We created maps to visualize sub-national autocorrelation and spatial clusters. The Global Moran’s I statistic was positive for each indicator (range: 0.41 to 0.68), and statistically significant (p <0.05), suggesting spatial autocorrelation across national borders, and highlighting the need to examine health indicators both across countries and within them. Patterns of substantial differences among contiguous subareas were apparent; the average intra-country difference for each indicator exceeded 20 percentage points. Clusters of cross-border associations were also apparent, facilitating the identification of hotspots and informing the allocation of resources to reduce child health inequity between and within countries. This study exposes differences in health indicators in contiguous geographic areas, indicating that specific regional and subnational, in addition to national, strategies to improve health and reduce health inequalities are warranted.

Estimation of ultrahigh resolution PM2.5 concentrations in urban areas using 160 m Gaofen-1 AOD retrievals

Satellite-derived aerosol optical depth (AOD) has been widely used to estimate ground-level PM2.5 concentrations due to its spatially continuous observation. However, the coarse spatial resolutions (e.g., 3 km, 6 km, or 10 km) of the primary satellite AOD products have weakness to capture the characteristics of urban-scale PM2.5 patterns. Moreover, high-resolution (e.g., 1 km) PM2.5 estimations are still unable to be related to the urban landscape or to small geographical units, which is crucial for analyzing the urban pollution structure. In this study, the daily PM2.5 concentrations were estimated using the new AOD data with a 160 m spatial resolution retrieved by the Gaofen-1 (GF) wide field of view (WFV) along with the nested linear mixed effects model and ancillary variables from the Weather Research & Forecasting (WRF) meteorological simulation data. The experiment was conducted in Wuhan, Beijing, and Shanghai, which suffers from severe atmospheric fine particle pollution in recent years. The proposed model performed well for both GF and Moderate Resolution Imaging Spectroradiometer (MODIS), with slight over-fitting and little spatial autocorrelation. Regarding to the GF PM2.5 estimation, model fitting yielded R2 values of 0.96, 0.91 and 0.95 and mean prediction error (MPE) of 10.13, 11.89 and 7.34 μg/m3 for Wuhan, Beijing and Shanghai, respectively. The site-based cross validation achieved R2 values of 0.92, 0.88 and 0.89, and MPE of 13.69, 16.76 and 12.59 μg/m3 for Wuhan, Beijing and Shanghai, respectively. The day-of-years based cross validation resulted in R2 of 0.54, 0.58 and 0.50, and MPE of 30.46, 27.12 and 31.58 μg/m3 for Wuhan, Beijing and Shanghai, respectively, indicating that it was practicable to estimate the GF PM2.5 in the days without enough AOD-PM2.5 matchups. The ultrahigh resolution PM2.5 estimations offer substantial advantages for providing finer spatially resolved PM2.5 trends. Additionally, it offers new approaches to locate main PM2.5 emission sources, evaluate the local PM2.5 contribution proportion, and quantify the daily PM2.5 emission level via remote sensing techniques. Along with the joint observations via other high-resolution satellites, the temporal resolution of GF PM2.5 will be further improved. In all, this study not only provides possibilities for further applications in the precise analysis of urban inner PM2.5 pollution patterns but also establishes a foundation for constructing a high-resolution satellite air monitoring network in China.

MP06-05 GEO-MAPPING AND SPATIAL ANALYSIS OF ENVIRONMENTAL EXPOSURES IN PATIENTS WITH BLADDER CANCER IN UPSTATE NEW YORK: AN EXPLORATORY STUDY

You have accessJournal of UrologyBladder Cancer: Epidemiology & Evaluation I1 Apr 2018MP06-05 GEO-MAPPING AND SPATIAL ANALYSIS OF ENVIRONMENTAL EXPOSURES IN PATIENTS WITH BLADDER CANCER IN UPSTATE NEW YORK: AN EXPLORATORY STUDY Hijab Khan, Zhu Jin, Ahmed Aly Hussein, Youssef Ahmed, Samantha Bulkilvish, Shelby Hall, Renuka Kannappan, Omer Rana, Peter Rogerson, Samina Raja, and Khurshid Guru Hijab KhanHijab Khan More articles by this author , Zhu JinZhu Jin More articles by this author , Ahmed Aly HusseinAhmed Aly Hussein More articles by this author , Youssef AhmedYoussef Ahmed More articles by this author , Samantha BulkilvishSamantha Bulkilvish More articles by this author , Shelby HallShelby Hall More articles by this author , Renuka KannappanRenuka Kannappan More articles by this author , Omer RanaOmer Rana More articles by this author , Peter RogersonPeter Rogerson More articles by this author , Samina RajaSamina Raja More articles by this author , and Khurshid GuruKhurshid Guru More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2018.02.197AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES The potential association of environmental exposures with bladder cancer is yet to be elucidated. We aimed to map patients treated with bladder cancer at our institution using Geographic Information Systems (GIS), and further attempted to identify and describe any environmental "hot spots." METHODS A retrospective review of our prospectively maintained database for patients with bladder cancer who visited our institution between 2006 and 2016 was performed. ArcGIS (v 10.4) was used to map patient residential addresses in Erie and Niagara counties in Upstate New York. We also conducted a “Hot Spot” analysis using the Getis-Ord Gi* statistic (using 90-99% confidence intervals). Analysis was conducted at the census block level (the smallest geographic unit used by the US Census Bureau) and accounted for population density of patients older than 50 years. Hot spots were further described in terms of water body quality (using reports from the New York State Department of Environmental Conservation) and industrial site presence (using data from the Environmental Protection Agency Facility Registry Service). RESULTS Out of 1543 patients with bladder cancer who visited our institution, 49% lived in Erie and Niagara counties. The mean age was 68 years (SD=13), 68% were males, and 55% used tobacco. Four hot spots were identified (Figure 1). Poor water quality was present in 3 and industrial sites were identified in 2 out of the 4 hot spots (Table 1). Water was contaminated with priority organic pollutants in one hot spot and pathogens in another. Additional suspected contaminants were present in 2 hot spots. Industrial sites produced specialty chemicals and processed food in one hot spot and fabricated metal at a second. CONCLUSIONS Spatial clustering of patients in 4 hotspots was identified in Erie and Niagara counties in Upstate New York. Within these hot spots, water quality and industrial sites of environmental concern were also identified. Future work will involve determining the relationship between these exposures, patient characteristics, and prevalence of bladder cancer. © 2018FiguresReferencesRelatedDetailsCited byYankelevich G, Dreher P, Taylor Z, Kim D, Buckley M, Larson S, Belkoff L, Bernstein G and Edwards D (2021) Socio-Environmental Conditions Associated with Geospatial Clusters of Urothelial Carcinoma: A Multi-Institutional AnalysisJournal of Urology, VOL. 206, NO. 6, (1390-1402), Online publication date: 1-Dec-2021. Volume 199Issue 4SApril 2018Page: e53-e54 Advertisement Copyright & Permissions© 2018MetricsAuthor Information Hijab Khan More articles by this author Zhu Jin More articles by this author Ahmed Aly Hussein More articles by this author Youssef Ahmed More articles by this author Samantha Bulkilvish More articles by this author Shelby Hall More articles by this author Renuka Kannappan More articles by this author Omer Rana More articles by this author Peter Rogerson More articles by this author Samina Raja More articles by this author Khurshid Guru More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...

"Big data" in economic history.

Big data is an exciting prospect for the field of economic history, which has long depended on the acquisition, keying, and cleaning of scarce numerical information about the past. This article examines two areas in which economic historians are already using big data - population and environment - discussing ways in which increased frequency of observation, denser samples, and smaller geographic units allow us to analyze the past with greater precision and often to track individuals, places, and phenomena across time. We also explore promising new sources of big data: organically created economic data, high resolution images, and textual corpora.

The distribution of district-level leprosy incidence in India is geometric-stable, consistent with subcriticality

Mathematical models predict that the community-level incidence of a controlled infectious disease across a region approaches a geometric distribution. This could hold over larger regions, if new cases remain proportional to existing cases. Leprosy has been disappearing for centuries, making an excellent candidate for testing this hypothesis. Here, we show the annual new case detection rate of leprosy in Indian districts to be consistent with a geometric distribution. For 2008–2013, goodness-of-fit testing was unable to exclude the geometric, and the shape parameter of the best fit negative binomial distribution was close to unity (0.95, 95% CI 0.87–1.03). Ramifications include that a district-level cross-sectional survey may reveal whether an infectious disease is headed towards elimination, that apparent outliers are expected and not necessarily representative of program failure, and that proportion 1/e of a small geographical unit may not meet a control threshold even when a larger area has.

Ranking composite Cancer Burden Indices for geographic regions: point and interval estimates.

To develop a composite Cancer Burden Index and produce 95% confidence intervals (CIs) as measures of uncertainties for the index. The Kentucky Cancer Registry has developed a cancer burden Rank Sum Index (RSI) to guide statewide comprehensive cancer control activities. However, lack of interval estimates for RSI limits its applications. RSI also weights individual measures with little inherent variability equally as ones with large variability. To address these issues, a Modified Sum Index (MSI) was developed to take into account of magnitudes of observed values. A simulation approach was used to generate individual and simultaneous 95% CIs for the rank MSI. An uncertainty measure was also calculated. At the Area Development Districts (ADDs) level, the ranks of the RSI and the MSI were almost identical, while larger variation was found at the county level. The widths of the CIs at the ADD level were considerably shorter than those at the county level. The measures developed for estimating composite cancer burden indices and the simulated CIs provide valuable information to guide cancer prevention and control effort. Caution should be taken when interpreting ranks from small population geographic units where the CIs for the ranks overlap considerably.

Health zones in Oregon: Exploring an ethical deliberation process in county public health

Summary Clackamas County, an administrative division in the State of Oregon on the west coast of the United States, has provided training in ethics and a decision-making framework to members of its public health advisory committee in an attempt to develop an ethical deliberation mechanism in public health. The goal of this mechanism is to ensure that a process exists for the Public Health Division to review value-laden issues relating to policies impacting the community. By providing guidance to county public health leadership upon request through an open and transparent review process, the intended outcome is a culture of ethical awareness and high ethical standards. The first deliberation of this body addressed the ethical concerns relating to the implementation of mapped “public health zones”: a project intended to more effectively communicate public health data of communities within the county in smaller geographic units and using geographic information system (GIS) technology.

Ability of ecological deprivation indices to measure social inequalities in a French cohort

BackgroundDespite the increasing interest in place effect to explain health inequalities, there is currently no consensus on which kind of area-based socioeconomic measures researchers should use to assess neighborhood socioeconomic position (SEP). The study aimed to evaluate the reliability of different area-based deprivation indices (DIs) in capturing socioeconomic residential conditions of French elderly women cohort.MethodsWe assessed area-based SEP using 3 DIs: Townsend Index, French European Deprivation Index (FEDI) and French Deprivation index (FDep), among women from E3N (Etude épidémiologique auprès des femmes de la Mutuelle Générale de l’Education Nationale). DIs were derived from the 2009 French census at IRIS level (smallest geographical units in France). Educational level was used to evaluate individual-SEP. To evaluate external validity of the 3 DIs, associations between two well-established socially patterned outcomes among French elderly women (smoking and overweight) and SEP, were compared. Odd ratios were computed with generalized estimating equations to control for clustering effects from participants within the same IRIS.ResultsThe analysis was performed among 63,888 women (aged 64, 47% ever smokers and 30% overweight). Substantial agreement was observed between the two French DIs (Kappa coefficient = 0.61) and between Townsend and FEDI (0.74) and fair agreement between Townsend and FDep (0.21). As expected among French elderly women, those with lower educational level were significantly less prone to be ever smoker (Low vs. High; OR [95% CI] = 0.43 [0.40–0.46]) and more prone to being overweight (1.89 [1.77–2.01]) than women higher educated. FDep showed expected associations at area-level for both smoking (most deprived vs. least deprived quintile; 0.77 [0.73–0.81]) and overweight (1.52 [1.44–1.62]). For FEDI opposite associations with smoking (1.13 [1.07–1.19]) and expected association with overweight (1.20 [1.13–1.28]) were observed. Townsend showed opposite associations to those expected for both smoking and overweight (1.51 [1.43–1.59]; 0.93 [0.88–0.99], respectively).ConclusionFDep seemed reliable to capture socioeconomic residential conditions of the E3N women, more educated in average than general French population. Results varied strongly according to the DI with unexpected results for some of them, which suggested the importance to test external validity before studying social disparities in health in specific populations.

Internal Migration and Land Use and Land Cover Changes in the Middle Mountains of Nepal

The movement of rural households from remote uplands to valley floors and to semiurban and urban areas (internal migration) is a common phenomenon in the middle mountain districts of Nepal. Understanding the causes and effects of internal migration is critical to the development and implementation of policies that promote land use planning and sustainable resource management. Using geospatial information technologies and social research methods, we investigated the causes and effects of internal migration on land use and land cover patterns in a western mountain district of Nepal between 1998 and 2013. The results show a decreasing number of households at high elevations (above 1400 m), where an increase in forest cover has been observed with a consequent decrease in agricultural land and shrub- or grassland. At lower elevations (below 1400 m), forest cover has remained constant over the last 25 years, and the agricultural land area has increased but has become geometrically complex to meet the diverse needs and living requirements of the growing population. Our findings indicate that internal migration plays an important role in shaping land use and land cover change in the middle mountains of Nepal and largely determines the resource management, utilization, and distribution patterns within a small geographic unit. Therefore, land use planning must take an integrated and interdisciplinary approach rather than considering social, environmental, and demographic information in isolation.

Predicting the Prevalence of Loneliness in Old Age Across Small Geographical Areas in England

1. Abstract Objective : This study predicts the prevalence of loneliness among people aged 65 or over across small geographical units in England. Method : It uses spatial two-level mixed-effects logistic regression model on data from a household survey and applies results to census data. Results : There is a huge variation of prevalence of loneliness among older people across England, which cannot be explained by local area characteristics, and are not spatially correlated. Discussion : This is the first attempt to estimate prevalence of loneliness among older people in England for small geographical units. Some limitations: The survey contains one question on loneliness with three categories; more detailed measures could not be used. Missing data could not could not be imputed as it was not possible to rule out non-randomness. Finally, ethnicity and sexual orientation were not included in the models due to data limitations. Public health implications : The method described in this paper can be used to design tailor-made interventions to address loneliness in later life in small geographical areas and to compare differences in prevalence across areas in a state, region or country. 2. Keywords : Loneliness; Local area; Later life

Effects of different building blocks designs on the statistical characteristics of Automated Zone-design Tool output areas

Prior to any census, the country usually gets demarcated into small geographic units called census enumeration areas, districts or blocks. In most countries, these small geographic units are also used for census dissemination. In cases where they are not used for census release, they are normally used as building blocks for developing output areas or they are aggregated to higher spatial levels in an effort to preserve privacy or confidentiality. Buildings blocks are therefore, of significant importance towards results that could be drawn from either aggregated higher level or from output areas developed using these small geographic areas. This paper aimed at evaluating the effects of different building blocks on the statistical characteristics of output areas generated using the Automated Zone-design Tool (AZTool) computer program. Different spatial layers (such as Enumeration Areas (EAs), Small Area Layers (SALs) and SubPlaces) from the 2001 census data were used as building blocks for the generation of census output areas with AZTool program in both rural and urban areas of South Africa. One way-Analysis of Variance (ANOVA) was also performed to determine statistical significance of the AZTool results. Results showed that the AZTool output areas generated from smaller areas (EAs and SALs) tend to be more homogeneous than the ones generated from larger areas (SubPlaces) when using dwelling type and geotype as homogeneity variables. The output areas from smaller areas also had narrower population distribution and more compact shapes than their counter-parts. In addition, the AZTool optimised output areas from the smaller areas allowed a clear distinction of the scale effects than output areas from larger areas. It was concluded that indeed different building blocks did have an impact on the statistical qualities of the AZTool optimised output areas in both rural and urban settings in South Africa.

County level incidence rates of chronic lymphocytic leukemia are associated with residential radon levels.

Aim:We previously reported that incidence rates for chronic lymphocytic leukemia (CLL) among US states are significantly correlated with levels of residential radon (RR). Because these correlations could be influenced by confounding and/or misclassification among large geographic units, we reinvestigated them using smaller geographic units that better reflect exposure and disease at the individual level.Methods:We examined the relationships between CLL and RR per county in 478 counties with publicly-available data.Results:After adjustment for ultraviolet radiation, a possible risk factor for CLL, county rates for CLL and RR were significantly correlated among males and females both together and separately (p < 0.0001).Conclusion:CLL is significantly associated with RR at the county level.

Patient navigation to reduce social inequalities in colorectal cancer screening participation: A cluster randomized controlled trial.

Despite free colorectal cancer screening in France, participation remains low and low socioeconomic status is associated with a low participation. Our aim was to assess the effect of a screening navigation program on participation and the reduction in social inequalities in a national-level organized mass screening program for colorectal cancer by fecal-occult blood test (FOBT). A multicenter (3 French departments) cluster randomized controlled trial was conducted over two years. The cluster was a small geographical unit stratified according to a deprivation index and the place of residence. A total of 14,556 subjects (72 clusters) were included in the control arm where the FOBT program involved the usual postal reminders, and 14,373 subjects (66 clusters) were included in the intervention arm. Intervention concerned only non-attended subjects with a phone number available defined as the navigable population. A screening navigator was added to the usual screening organization to identify and eliminate barriers to CRC screening with personalized contact. The participation rate by strata increased in the intervention arm. The increase was greater in affluent strata than in deprived ones. Multivariate analyses demonstrated that the intervention mainly with phone navigation increased individual participation (OR=1.19 [1.10, 1.29]) in the navigable population. For such interventions to reduce social inequalities in a country with a national level organized mass screening program, they should first be administered to deprived populations, in accordance with the principle of proportionate universalism. ClinicalTrials.gov Identifier: NCT01555450.

Beyond monocentricity: examining the spatial distribution of employment in Tehran metropolitan region, Iran

ABSTRACTThis research examines the spatial distribution of employment in Tehran metropolitan region as one of the most populated regions in West Asia. For this aim, our approach includes three steps; first, the paper investigates the level of monocentricity or the primacy of the main core, then, the paper utilises various methodologies to identify the employment subcenters in the region; and finally, the importance of identified centres is estimated by polycentric employment function. To do this, data obtained from Statistical Centre of Iran for 2006 and 2011 is provided in sub-district level, the smallest geographical unit. Results revealed that monocentric model is not able to explain the spatial distribution of employment in TMR; also, the main core loses its importance with the passage of time. Applying different methodologies for TMR identified 3 subcenters in 2006; whereas, it reached to 7 subcenters in 2011. In the last step, the deployed polycentric employment function explained 42% and 51% of total employment distribution throughout TMR in 2006 and 2011 respectively.

Searching for the existence of entrepreneurial ecosystems: a regional cross-section growth regression approach

In this paper, we propose a method by which the entrepreneurial ecosystem, if present, reveals itself in the data. We first follow the literature and define the entrepreneurial ecosystem as a multidimensional set of interacting factors that moderate the effect of entrepreneurial activity on economic growth. The quality of such an ecosystem, by its multidimensionality, is impossible to measure directly. But so defined, we argue that variation in entrepreneurial ecosystem quality should result in variation in the estimated marginal effect of entrepreneurial activity on economic growth. Testing for such variation is possible using a combination of a multilevel growth regression and latent class analysis. We motivate and validate our approach in simulated data before illustrating its applicability in a data set covering 107 European NUTS1-2 regions across 16 EU member states. For this dataset, we cannot reject the hypothesis of a homogeneous contribution of entrepreneurship to regional growth. That is, in this dataset, we find no evidence of statistically significant heterogeneity in the estimated slope coefficients for entrepreneurial activity across regions. There are several possible explanations for this negative result. The two we deem most likely are first that the NUTS1-2 level may not be disaggregated enough to coincide with the relevant boundaries of the entrepreneurial ecosystem. We suspect our method would reveal significant differences across smaller geographical units, but the data unfortunately do not yet allow us to empirically test this hypothesis in a multi-country regional analysis. The second possible explanation is that the growth rates from 2006 to 2014 coincided with the global financial and the European crisis, and during this time, the effect of entrepreneurship on (long-run average) growth overall has been obscured. Our simulations also suggested a third reason. If measurement error is large (in the order of 33 or 0.015% point annual GDP-growth), the effects may also have been obscured.

Geographic distributions of motor neuron disease mortality and well water use in U.S. counties

Objective: We recently reported that U.S. mortality rates for motor neuron disease (MND) at the level of the state are associated with well water use. However, data at the state level may not accurately reflect data at the individual level. We therefore examined the association between MND mortality and well water use utilizing data from smaller geographic units that may better reflect exposure and disease at the individual level. Methods: We used data on age-adjusted MND mortality rates at the level of the county, obtained from the CDC, and corresponding data on the prevalence of well water use, obtained from the U.S. Geological Survey. Data were analyzed by multivariate linear regression and by Getis-Ord Gi*, a measure of spatial clustering. Results: Age-adjusted mortality rates for MND in 923 U.S. counties were significantly correlated with the prevalence of well water (p < 0.0001). ‘Hot spots’ of MND mortality were significantly associated with ‘hot spots’ of well water use (p < 0.0005). Conclusions: These findings support the hypothesis that an agent present in well water plays an etiologic role in ALS. Further study of water use among individuals with ALS is warranted.

Built environmental factors and adults' travel behaviors: Role of street layout and local destinations

Street layout is consistently associated with adults' travel behaviors, however factors influencing this association are unclear.We examined associations of street layout with travel behaviors: walking for transport (WT) and car use; and, the extent to which these relationships may be accounted for by availability of local destinations.A 24-h travel diary was completed in 2009 by 16,345 adult participants of the South-East Queensland Household Travel Survey, Australia. Three travel-behavior outcomes were derived: any home-based WT; over 30min of home-based WT; and, over 60min of car use. For street layout, a space syntax measure of street integration was calculated for each Statistical Area 1 (SA1, the smallest geographic unit in Australia). An objective measure of availability of destinations – Walk Score – was also derived for each SA1. Logistic regression examined associations of street layout with travel behaviors. Mediation analyses examined to what extent availability of destinations explained the associations.Street integration was significantly associated with travel behaviors. Each one-decile increment in street integration was associated with an 18% (95%CI: 1.15, 1.21) higher odds of any home-based WT; a 10% (95%CI: 1.06, 1.15) higher odds of over 30min of home-based WT; and a 5% (95%CI: 0.94, 0.96) lower odds of using a car over 60min. Local destinations partially mediated the effects of street layout on travel behaviors.Well-connected street layout contributes to active travel partially through availability of more local destinations. Urban design strategies need to address street layout and destinations to promote active travel among residents.

Measuring the vulnerability of disadvantaged populations during hurricane evacuation

In this paper, the extent to which the potentially transit-dependent portion of the population is vulnerable to the effects of a hurricane is estimated. The vulnerability of an area is defined as a composite measure of the proportion of disadvantaged persons, distance to transit, and flooding potential of people within an area. Unlike past studies which have focused on the vulnerability of the population in relatively large geographic areas, this study estimates the vulnerability of the population in 30 m × 30 m areas as defined in the National Land Cover Database. Population estimates from the national census at block level are disaggregated to the 30 m × 30 m units using a modified dasymetric mapping method in ArcGIS. The modified mapping method assigns population to each small areal unit using weights estimated by regressing the area of each land use in a census block against the population in that block. The coefficients in the regression analysis are “weights” associating population with each land use, and are used to distribute the population in each census block to the small geographic units based on their land use. In a case study of New Orleans, the results show that some areas are not well served by the existing transit pickup locations, as evidenced by their high vulnerability scores. Reassignment of pickup point locations to cover higher vulnerability score areas was investigated using integer linear programming. The results show that the optimally located pickup points serve areas with a larger average vulnerability score than the current pickup points in the study area. The method appears to be helpful in identifying vulnerable areas that, subsequently, could receive improved hurricane evacuation service in the future.

The spatial structure of chronic morbidity: evidence from UK census returns.

BackgroundDisease prevalence models have been widely used to estimate health, lifestyle and disability characteristics for small geographical units when other data are not available. Yet, knowledge is often lacking about how to make informed decisions around the specification of such models, especially regarding spatial assumptions placed on their covariance structure. This paper is concerned with understanding processes of spatial dependency in unexplained variation in chronic morbidity.Methods2011 UK census data on limiting long-term illness (LLTI) is used to look at the spatial structure in chronic morbidity across England and Wales. The variance and spatial clustering of the odds of LLTI across local authority districts (LADs) and middle layer super output areas are measured across 40 demographic cross-classifications. A series of adjacency matrices based on distance, contiguity and migration flows are tested to examine the spatial structure in LLTI. Odds are then modelled using a logistic mixed model to examine the association with district-level covariates and their predictive power.ResultsThe odds of chronic illness are more dispersed than local age characteristics, mortality, hospitalisation rates and chance alone would suggest. Of all adjacency matrices, the three-nearest neighbour method is identified as the best fitting. Migration flows can also be used to construct spatial weights matrices which uncover non-negligible autocorrelation. Once the most important characteristics observable at the LAD-level are taken into account, substantial spatial autocorrelation remains which can be modelled explicitly to improve disease prevalence predictions.ConclusionsSystematic investigation of spatial structures and dependency is important to develop model-based estimation tools in chronic disease mapping. Spatial structures reflecting migration interactions are easy to develop and capture autocorrelation in LLTI. Patterns of spatial dependency in the geographical distribution of LLTI are not comparable across ethnic groups. Ethnic stratification of local health information is needed and there is potential to further address complexity in prevalence models by improving access to disaggregated data.Electronic supplementary materialThe online version of this article (doi:10.1186/s12942-016-0057-5) contains supplementary material, which is available to authorized users.