County-level Poverty Estimates for the Contiguous United States, 2001, 2005

Journal of Maps, Volume 8, Issue 4, December 2012, pages 334-339

Joseph J.A. Campbell and Corey Sparks

“Efforts to estimate various sociodemographic variables in small geographical areas are proving difficult with the replacement of the Census long form with the American Community Survey (ACS). Researchers interested in sub-national demographic processes have generally relied on Census long form data products in order to answer research questions. ACS data products promise to begin providing up-to-date profiles of the nation’s population and economy; however, unit and item-level non-response in the ACS have left researchers with gaps in sub-national coverage resulting in unstable and unreliable estimates for basic demographic measures. Borrowing information from neighboring areas and across time with a spatiotemporal smoothing process based on Bayesian statistical methods, it is possible to generate more stable and accurate estimates of rates for geographic areas not represented in the ACS. This research assesses this spatiotemporal smoothing process in its ability to derive estimates of poverty rates at the county level for the contiguous United States. These estimates are then compared to more traditional estimates from the Census, and error rates are calculated to evaluate the practical application of this smoothing method. The resulting summary choropleth map displays the Bayesian estimates of county-level poverty at a scale of 1 to 12,000,000 along with summary choropleth maps of the more traditional estimates at a scale of 1 to 37,000,000 for 2001 and 2005. Error rates indicate that the Bayesian estimates of county-level poverty produced by our succinct model produce results similar to more complex traditional estimates produced by the Census.”

Mapping Spatial Variations of Health Insurance Coverage in the Coastal Bend, Texas

Journal of MapsJournal of Maps, Volume 8, Issue 4, December 2012, pages 349-353

Yuxia Huang & Pamela Meyer

“A 2010 Health Needs Assessment for 15 counties of the Coastal Bend in the state of Texas indicates limited access to health care services and health insurance coverage is a main potential barrier to health care for some segments of the Coastal Bend population. The purpose of this paper is to obtain geographical sight of the health insurance coverage. The hypothesis is that the health insurance coverage by racial and ethnic groups would vary spatially. Data came from the local hospital systems and included 145,669 patient visits from 1 September 2007 through 31 August 2009. A series of maps were produced showing financial class categories for both Hispanics and Whites adults by combining the cross-tabulations of patient data and estimated population both at the zip code level. The maps show that the health insurance coverage disparities vary spatially within zip codes in the Coastal Bend. Moreover, Hispanic and White adult patients do not follow the same pattern of spatial distribution.”

Fusing Remote Sensing with Sparse Demographic Data for Synthetic Population Generation: An Algorithm and Application to Rural Afghanistan

International Journal of Geographical Information ScienceInternational Journal of Geographical Information Science, published online 19 November 2012

Seyed M. Mussavi Rizi, Maciej M. Łatek, and Armando Geller

“We develop a new algorithm for population synthesis that fuses remote-sensing data with partial and sparse demographic surveys. The algorithm addresses non-binding constraints and complex sampling designs by translating population synthesis into a computationally efficient procedure for constrained network growth. As a case, we synthesize the rural population of Afghanistan, validate the algorithm with in-sample and out-of-sample tests, examine the variability of algorithm outputs over k-nearest neighbor manifolds, and show the responsiveness of our algorithm to additional data as a constraint on marginal population counts.”

Typhoid Fever and Its Association with Environmental Factors in the Dhaka Metropolitan Area of Bangladesh: A Spatial and Time-Series Approach

PLoS Negl Trop Dis PLOS Neglected Tropical Diseases, 24 January 2013

Ashraf M. Dewan, Robert Corner, Masahiro Hashizume, and Emmanuel T. Ongee

“Typhoid fever is a major cause of death worldwide with a major part of the disease burden in developing regions such as the Indian sub-continent. Bangladesh is part of this highly endemic region, yet little is known about the spatial and temporal distribution of the disease at a regional scale. This research used a Geographic Information System to explore, spatially and temporally, the prevalence of typhoid in Dhaka Metropolitan Area (DMA) of Bangladesh over the period 2005–9. This paper provides the first study of the spatio-temporal epidemiology of typhoid for this region. The aims of the study were: (i) to analyse the epidemiology of cases from 2005 to 2009; (ii) to identify spatial patterns of infection based on two spatial hypotheses; and (iii) to determine the hydro-climatological factors associated with typhoid prevalence. Case occurrences data were collected from 11 major hospitals in DMA, geocoded to census tract level, and used in a spatio-temporal analysis with a range of demographic, environmental and meteorological variables. Analyses revealed distinct seasonality as well as age and gender differences, with males and very young children being disproportionately infected. The male-female ratio of typhoid cases was found to be 1.36, and the median age of the cases was 14 years. Typhoid incidence was higher in male population than female (χ2 = 5.88, p<0.05). The age-specific incidence rate was highest for the 0–4 years age group (277 cases), followed by the 60+ years age group (51 cases), then there were 45 cases for 15–17 years, 37 cases for 18–34 years, 34 cases for 35–39 years and 11 cases for 10–14 years per 100,000 people. Monsoon months had the highest disease occurrences (44.62%) followed by the pre-monsoon (30.54%) and post-monsoon (24.85%) season.

Spatial regression between typhoid incidence (per 100,000 people) and distance to water bodies.

Spatial regression between typhoid incidence (per 100,000 people) and distance to water bodies. A) Shows spatial distribution of the t-value, B) shows the parameter estimates.

“The Student’s t test revealed that there is no significant difference on the occurrence of typhoid between urban and rural environments (p>0.05). A statistically significant inverse association was found between typhoid incidence and distance to major waterbodies. Spatial pattern analysis showed that there was a significant clustering of typhoid distribution in the study area. Moran’s I was highest (0.879; p<0.01) in 2008 and lowest (0.075; p<0.05) in 2009. Incidence rates were found to form three large, multi-centred, spatial clusters with no significant difference between urban and rural rates. Temporally, typhoid incidence was seen to increase with temperature, rainfall and river level at time lags ranging from three to five weeks. For example, for a 0.1 metre rise in river levels, the number of typhoid cases increased by 4.6% (95% CI: 2.4–2.8) above the threshold of 4.0 metres (95% CI: 2.4–4.3). On the other hand, with a 1°C rise in temperature, the number of typhoid cases could increase by 14.2% (95% CI: 4.4–25.0).”

Decelerating Spread of West Nile Virus by Percolation in a Heterogeneous Urban Landscape

PLoS Comput BiolPLoS Computational Biology 7(7), 2011

Krisztian Magori, Waheed I. Bajwa, Sarah Bowden, and John M. Drake

“Vector-borne diseases are emerging and re-emerging in urban environments throughout the world, presenting an increasing challenge to human health and a major obstacle to development. Currently, more than half of the global population is concentrated in urban environments, which are highly heterogeneous in the extent, degree, and distribution of environmental modifications. Because the prevalence of vector-borne pathogens is so closely coupled to the ecologies of vector and host species, this heterogeneity has the potential to significantly alter the dynamical systems through which pathogens propagate, and also thereby affect the epidemiological patterns of disease at multiple spatial scales. One such pattern is the speed of spread. Whereas standard models hold that pathogens spread as waves with constant or increasing speed, we hypothesized that heterogeneity in urban environments would cause decelerating travelling waves in incipient epidemics.

The spatial structure of annual WNV outbreaks in NYC, demonstrated for the year of 2003.

The spatial structure of annual WNV outbreaks in NYC, demonstrated for the year of 2003. Speed of WNV spread was estimated from point locations of WNV-positive mosquito pools (circles) and WNV-positive dead birds (triangles). Dark and light cyan areas represent transmission-inhibiting and transmission-promoting land-cover types. The black crosses represent the approximate location of Central Park, La Guardia Airport and the John F. Kennedy International Airport respectively, where NOAA collects weather data. The first five locations where WNV was detected in 2003 are labeled as A, B, C, D, and E, respectively. The first estimate of wave-speed was calculated using the convex hull method as (1) the increase of the square root area of the polygon encompassing ABCDE (black) relative to the square root area of the polygon encompassing ABCD (red) locations (convex hull method).

“To test this hypothesis, we analysed data on the spread of West Nile virus (WNV) in New York City (NYC), the 1999 epicentre of the North American pandemic, during annual epizootics from 2000–2008. These data show evidence of deceleration in all years studied, consistent with our hypothesis. To further explain these patterns, we developed a spatial model for vector-borne disease transmission in a heterogeneous environment. An emergent property of this model is that deceleration occurs only in the vicinity of a critical point. Geostatistical analysis suggests that NYC may be on the edge of this criticality. Together, these analyses provide the first evidence for the endogenous generation of decelerating travelling waves in an emerging infectious disease. Since the reported deceleration results from the heterogeneity of the environment through which the pathogen percolates, our findings suggest that targeting control at key sites could efficiently prevent pathogen spread to remote susceptible areas or even halt epidemics.”

Global Mapping of Infectious Disease

Philosophical Transactions of the Royal Society B, 04 February 2013

Simon I. Hay, Katherine E. Battle, David M. Pigott, David L. Smith, Catherine L. Moyes, Samir Bhatt, John S. Brownstein, Nigel Collier, Monica F. Myers, Dylan B. George, and Peter W. Gething

“The primary aim of this review was to evaluate the state of knowledge of the geographical distribution of all infectious diseases of clinical significance to humans. A systematic review was conducted to enumerate cartographic progress, with respect to the data available for mapping and the methods currently applied. The results helped define the minimum information requirements for mapping infectious disease occurrence, and a quantitative framework for assessing the mapping opportunities for all infectious diseases. This revealed that of 355 infectious diseases identified, 174 (49%) have a strong rationale for mapping and of these only 7 (4%) had been comprehensively mapped.

A schematic overview of a niche/occurrence mapping process (for example boosted regression trees (BRT)) that uses pseudo-absence data guided by expert opinion. Consensus based definitive extent layers of infectious disease occurrence at the national level (a) are combined with accurately geo-positioned occurrence (presence) locations (b) to generate pseudo-absence data (c). The presence (b) and pseudo-absence data (c) are then used in the BRT analyses, alongside a suite of environmental covariates (d ) to predict the probability of occurrence of the target disease (e).

A schematic overview of a niche/occurrence mapping process (for example boosted regression trees (BRT)) that uses pseudo-absence data guided by expert opinion. Consensus based definitive extent layers of infectious disease occurrence at the national level (a) are combined with accurately geo-positioned occurrence (presence) locations (b) to generate pseudo-absence data (c). The presence (b) and pseudo-absence data (c) are then used in the BRT analyses, alongside a suite of environmental covariates (d ) to predict the probability of occurrence of the target disease (e).

“A variety of ambitions, such as the quantification of the global burden of infectious disease, international biosurveillance, assessing the likelihood of infectious disease outbreaks and exploring the propensity for infectious disease evolution and emergence, are limited by these omissions. An overview of the factors hindering progress in disease cartography is provided. It is argued that rapid improvement in the landscape of infectious diseases mapping can be made by embracing non-conventional data sources, automation of geo-positioning and mapping procedures enabled by machine learning and information technology, respectively, in addition to harnessing labour of the volunteer ‘cognitive surplus’ through crowdsourcing.”

Developing Context-sensitive Livability Indicators for Transportation Planning: A Measurement Framework

Journal of Transport GeographyJournal of Transport Geography, Volume 26, January 2013, Pages 51–64

Harvey J. Miller, Frank Witlox, and Calvin P. Tribby

“Highlights:

  • Community livability concepts are receiving new emphases in transportation planning.
  • This paper provides a framework for constructing quantitative livability indicators.
  • We critically review indicator construction methods based on multicriteria analysis.
  • We discuss methods for capturing diverse stakeholder perspectives and geographic context.
  • We also discuss strategies for integrating indicators into transportation planning.

“New emphases on livability and sustainability are creating demands for measuring and applying these concepts in transportation policy and planning. However, livability and sustainability are complex, multidimensional concepts that require careful measurement if they are to be applied meaningfully in plan evaluation and benchmarking.

1-s2.0-S0966692312002189-gr1

Conceptual foundation of multidimensional indicators.

“This paper provides a framework for constructing and applying quantitative livability and sustainability indicators. In addition to critically reviewing principles of constructing indicators describing a multidimensional concept such as livability or sustainability, we also discuss methods for capturing local context, a critical feature for transportation planning. Specifically, we review methods for incorporating diverse stakeholder perspectives into indicator construction and spatial analytic tools for geographic entities and relationships. We also discuss spatial decision support systems and the Geodesign concept for organizing these tools and technologies as well as integrating livability indicators into the overall planning process.”