Spatial Analysis

Temporal and Spatial Analysis of Neural Tube Defects and Detection of Geographical Factors in Shanxi Province, China

/ Matt Artz

PLOS_ONEPLOS ONE, Published 21 April 2016

By Yilan Liao, Yan Zhang, Lei He, Jinfeng Wang, Xin Liu, Ningxu Zhang, and Bing Xu

“Background: Neural tube defects (NTDs) are congenital birth defects that occur in the central nervous system, and they have the highest incidence among all birth defects. Shanxi Province in China has the world’s highest rate of NTDs. Since the 1990s, China’s government has worked on many birth defect prevention programs to reduce the occurrence of NTDs, such as pregnancy planning, health education, genetic counseling, antenatal ultrasonography and serological screening. However, the rate of NTDs in Shanxi Province is still higher than the world’s average morbidity rate after intervention. In addition, Shanxi Province has abundant coal reserves, and is the largest coal production province in China. The objectives of this study are to determine the temporal and spatial variation of the NTD rate in rural areas of Shanxi Province, China, and identify geographical environmental factors that were associated with NTDs in the risk area.

“Methods: In this study, Heshun County and Yuanping County in Shanxi Province, which have high incidence of NTDs, were selected as the study areas. Two paired sample T test was used to analyze the changes in the risk of NTDs from the time dimension. Ripley’s k function and spatial filtering were combined with geographic information system (GIS) software to study the changes in the risk of NTDs from the spatial dimension. In addition, geographical detectors were used to identify the risk geographical environmental factors of NTDs in the study areas, especially the areas close to the coal sites and main roads.

Cluster areas of Neural Tube Defects in Heshun County and Yuanping County.

Cluster areas of Neural Tube Defects in Heshun County and Yuanping County.

“Results: In both Heshun County and Yuanping County, the incidence of NTDs was significantly (P<0.05) reduced after intervention. The results from spatial analysis showed that significant spatial heterogeneity existed in both counties. NTD clusters were still identified in areas close to coal sites and main roads after interventions. This study also revealed that the elevation, fault and soil types always had a larger influence on the incidence of NTDs in our study areas. In addition, distance to the river was a risk factor of NTDs in areas close to the coal sites and main roads.

“Conclusion: The existing interventions may have played an important role to reduce the incidence of NTDs. However, there is still spatial heterogeneity in both counties after using the traditional intervention methods. The government needs to take more measures to strengthen the environmental restoration to prevent the occurrence of NTDs, especially those areas close to coal sites and main roads. The outcome of this research provides an important theoretical basis and technical support for the government to prevent the occurrence of NTDs.”

Risks of developing breast and colorectal cancer in association with incomes and geographic locations in Texas: a retrospective cohort study

/ Matt Artz

bmc cancerBMC Cancer 2016 16:294, Published 26 April 2016

By Zheyu Liu, Kai Zhang, and Xianglin L. Du

“Background: No study has been conducted to investigate the spatial pattern and association of socioeconomic status (such as income) with breast and colorectal cancer incidence in Texas, United States. This study aimed to determine whether median household income was associated with the risk of developing breast and colorectal cancer in Texas and to identify higher cancer risks by race/ethnicity and geographic areas.

Methods:This was a retrospective cohort study with an ecological component in using aggregated measures at the county level. We identified 243,677 women with breast cancer and 155,534 men and women with colorectal cancer residing in 254 counties in Texas in 1995–2011 from the public-use dataset of Texas Cancer Registry. The denominator population and median household income at the county level was obtained from the U.S. Bureau of the Census. Cancer incidence rates were calculated as number of cases per 100,000 persons and age-adjusted using the 2000 US population data. We used the ArcGIS v10.1 (geographic information system software) to identify multiple clustered counties with high and low cancer incidences in Texas.

Geographic variations of colorectal cancer incidence adjusted for age and median household income in Texas, 1995–2011

Geographic variations of colorectal cancer incidence adjusted for age and median household income in Texas, 1995–2011

Results: Age-adjusted breast cancer incidence rate in the highest median income quintile group was 151.51 cases per 100,000 in 2008–2011 as compared to 98.95 cases per 100,000 in the lowest median income quintile group. The risk of colorectal cancer appeared to decrease with increasing median income in racial/ethnic population. Spatial analysis revealed the significant low breast cancer incidence cluster regions located in southwest US-Mexico border counties in Texas.

Conclusions: This study demonstrated that higher income was associated with an increased risk of breast cancer and a decreased risk of colorectal cancer in Texas. There were geographic variations with cancer incidence clustered in high risk areas in Texas. Future studies may need to explore more factors that might explain income and cancer risk associations and their geographic variations.”

Geostatistical interpolation model selection based on ArcGIS and spatio-temporal variability analysis of groundwater level in piedmont plains, northwest China

/ Matt Artz / 1 Comment

SpringerPlus, Published 11 April 2016

By Yong Xiao, Xiaomin Gu, Shiyang YinEmail author, Jingli Shao, Yali Cui, Qiulan Zhang, and Yong Niu

“Based on the geo-statistical theory and ArcGIS geo-statistical module, datas of 30 groundwater level observation wells were used to estimate the decline of groundwater level in Beijing piedmont. Seven different interpolation methods (inverse distance weighted interpolation, global polynomial interpolation, local polynomial interpolation, tension spline interpolation, ordinary Kriging interpolation, simple Kriging interpolation and universal Kriging interpolation) were used for interpolating groundwater level between 2001 and 2013. Cross-validation, absolute error and coefficient of determination (R2) was applied to evaluate the accuracy of different methods.

Groundwater level drawdown during 2001 and 2013.

Groundwater level drawdown during 2001 and 2013.

“The result shows that simple Kriging method gave the best fit. The analysis of spatial and temporal variability suggest that the nugget effects from 2001 to 2013 were increasing, which means the spatial correlation weakened gradually under the influence of human activities. The spatial variability in the middle areas of the alluvial–proluvial fan is relatively higher than area in top and bottom. Since the changes of the land use, groundwater level also has a temporal variation, the average decline rate of groundwater level between 2007 and 2013 increases compared with 2001–2006. Urban development and population growth cause over-exploitation of residential and industrial areas. The decline rate of the groundwater level in residential, industrial and river areas is relatively high, while the decreasing of farmland area and development of water-saving irrigation reduce the quantity of water using by agriculture and decline rate of groundwater level in agricultural area is not significant.”

Locating Chicago’s Charter Schools: A Socio-Spatial Analysis

/ Matt Artz / 2 Comments

epaaEducation Policy Analysis Archives, Volume 24, Number 24, 14 March 2016

By Jennifer C. LaFleur

“This project contributes to the body of research examining the implications of the geographic location of charter schools for student access, especially in high-poverty communities. Using geographic information systems (GIS) software, this paper uses data from the U.S. Census American Community Survey to identify the socioeconomic characteristics of the census tracts in which Chicago’s charter schools tend to locate. Echoing the findings of other researchers who have examined charter school locational patterns, the present analyses found evidence of a “ceiling effect” by which many charter schools appear to locate in Chicago’s higher-needs census tracts, broadly cast, but avoid locating directly within those that are highest-need.

Map including portions of the following neighborhoods: South Austin, West Garfield Park, and West Humboldt Park. Yellow flags represent the location of charter schools. Shading reflects the number of standard deviation units the census tract’s socioeconomic need index score was from the city mean. Census tracts shaded in the lightest blue represent areas of lowest socioeconomic need, those shaded in the darkest blue represent areas of highest socioeconomic need.

Map including portions of the following neighborhoods: South Austin, West Garfield Park, and West Humboldt Park. Yellow flags represent the location of charter schools. Shading reflects the number of standard deviation units the census tract’s socioeconomic need index score was from the city mean. Census tracts shaded in the lightest blue represent areas of lowest socioeconomic need, those shaded in the darkest blue represent areas of highest socioeconomic need.

“The findings suggest that because Chicago’s charter schools face per-pupil expenditures that are often up to 20% less than those of traditional public schools, they may strategically leverage location to help shape student enrollment. By frequently locating near, but not directly within highest-need communities, charter schools may find it easier to attract a quorum of relatively higher achieving students who are less expensive to educate, therefore increasing their chances of meeting academic benchmarks and retaining their charters. By extending the findings of other researchers to the context of Chicago—where charters represent an ever-increasing share of the public school market—the present analyses may inform future revisions to the policies governing the authorization of charter schools in Chicago, with the goal of increasing access for highest-need students. ”

Where Are Socioeconomically Deprived Immigrants Located in Chile? A Spatial Analysis of Census Data Using an Index of Multiple Deprivation from the Last Three Decades

/ Matt Artz / 1 Comment

“Introduction and Purpose of the Study

Immigrants in Chile have diverse characteristics and include socioeconomically deprived populations. The location of socioeconomically deprived immigrants is important for the development of public policy intelligence at the local and national levels but their areas of residence have not been mapped in Chile. This study explored the spatial distribution of socioeconomic deprivation among immigrants in Chile, 1992–2012, and compared it to the total population.

Material and Methods

Areas with socioeconomically deprived populations were identified with a deprivation index which we developed modelled upon the Index of Multiple Deprivation (IMD) for England. Our IMD was based upon the indicators of unemployment, low educational level (primary) and disability from Census data at county level for the three decades 1992, 2002 and 2012, for 332, 339 and 343 counties respectively. We developed two versions of the IMD one based on disadvantage among the total population and another focused upon the circumstances of immigrants only. We generated a spatial representation of the IMD using GIS, for the overall IMD score and for each dimension of the index, separately. We also compared the immigrants´ IMD to the total population´s IMD using Pearson´s correlation test.

journal.pone.0146047.g002.PNG

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Results

Results showed that socioeconomically deprived immigrants tended to be concentrated in counties in the northern and central area of Chile, in particular within the Metropolitan Region of Santiago. These were the same counties where there was the greatest concentration of socioeconomic deprivation for the total population during the same time periods. Since 1992 there have been significant change in the location of the socioeconomically deprived populations within the Metropolitan Region of Santiago with the highest IMD scores for both the total population and immigrants becoming increasingly concentrated in the central and eastern counties of the Region.

Conclusion

This is the first study analysing the spatial distribution of socioeconomic deprivation among international immigrants and the total population in a Latin American country. Findings could inform policy makers about location of areas of higher need of social protection in Chile, for both immigrants and the total resident population in the country.”

Spatial Methods for Infectious Disease Outbreak Investigations: Systematic Literature Review

/ Matt Artz

ESEurosurveillance, Volume 20, Issue 39, 01 October 2015

By CM Smith, SC Le Comber, H Fry, M Bull, S Leach, and AC Hayward

“Investigations of infectious disease outbreaks are conventionally framed in terms of person, time and place. Although geographic information systems have increased the range of tools available, spatial analyses are used relatively infrequently. We conducted a systematic review of published reports of outbreak investigations worldwide to estimate the prevalence of spatial methods, describe the techniques applied and explore their utility.

Locations of outbreak investigations using spatial methods by country and continent (n = 80)

Locations of outbreak investigations using spatial methods by country and continent (n = 80)

“We identified 80 reports using spatial methods published between 1979 and 2013, ca 0.4% of the total number of published outbreaks. Environmental or waterborne infections were the most commonly investigated, and most reports were from the United Kingdom. A range of techniques were used, including simple dot maps, cluster analyses and modelling approaches. Spatial tools were usefully applied throughout investigations, from initial confirmation of the outbreak to describing and analysing cases and communicating findings. They provided valuable insights that led to public health actions, but there is scope for much wider implementation and development of new methods.”

Exploring Spatiotemporal Trends in Commercial Fishing Effort of an Abalone Fishing Zone: A GIS-Based Hotspot Model

/ Matt Artz

PLOS_ONEPLOS One, Published 20 May 2015

By M. Ali Jalali, Daniel Ierodiaconou, Harry Gorfine, Jacquomo Monk, and Alex Rattray
“Assessing patterns of fisheries activity at a scale related to resource exploitation has received particular attention in recent times. However, acquiring data about the distribution and spatiotemporal allocation of catch and fishing effort in small scale benthic fisheries remains challenging. Here, we used GIS-based spatio-statistical models to investigate the footprint of commercial diving events on blacklip abalone (Haliotis rubra) stocks along the south-west coast of Victoria, Australia from 2008 to 2011. Using abalone catch data matched with GPS location we found catch per unit of fishing effort (CPUE) was not uniformly spatially and temporally distributed across the study area. Spatial autocorrelation and hotspot analysis revealed significant spatiotemporal clusters of CPUE (with distance thresholds of 100’s of meters) among years, indicating the presence of CPUE hotspots focused on specific reefs.
Cumulative hotspot distribution map. Cumulative CPUE hotspot map overlays (based on the number of years that CPUE was clustered) for (A) Julia Bank and (B) Discovery Bay over LiDAR derived hillshade.

Cumulative hotspot distribution map. Cumulative CPUE hotspot map overlays (based on the number of years that CPUE was clustered) for (A) Julia Bank and (B) Discovery Bay over LiDAR derived hillshade.

“Cumulative hotspot maps indicated that certain reef complexes were consistently targeted across years but with varying intensity, however often a relatively small proportion of the full reef extent was targeted. Integrating CPUE with remotely-sensed light detection and ranging (LiDAR) derived bathymetry data using generalized additive mixed model corroborated that fishing pressure primarily coincided with shallow, rugose and complex components of reef structures. This study demonstrates that a geospatial approach is efficient in detecting patterns and trends in commercial fishing effort and its association with seafloor characteristics.”

Associations between residence at birth and mental health disorders: a spatial analysis of retrospective cohort data

/ Matt Artz

BMC Public Health
BMC Public Health, (2015) 15:688

By Kate Hoffman, Ann Aschengrau, Thomas F. Webster, Scott M. Bartell, and Verónica M. Vieira

“Background: Mental healthdisorders impact approximately one in four US adults. While their causes are likely multifactorial, prior research has linked the risk of certain mental health disorders to prenatal and early childhood environmental exposures, motivating a spatial analysis to determine whether risk varies by birth location.

“Methods: We investigated the spatial associations between residence at birth and odds of depression, bipolar disorder, and post-traumatic stress disorder (PTSD) in a retrospective cohort (Cape Cod, Massachusetts, 1969–1983) using generalized additive models to simultaneously smooth location and adjust for confounders. Birth location served as a surrogate for prenatal exposure to the combination of social and environmental factors related to the development of mental illness. We predicted crude and adjusted odds ratios (aOR) for each outcome across the study area. The results were mapped to identify areas of increased risk.

Geographic distribution of PTSD vs. no reported mental illness from analyses using the optimal span size for each model [unadjusted (a) and adjusted for sex, year of birth, family history of mental health diagnosis, father’s occupation, mother’s educational attainment, maternal smoking during pregnancy, and pre/postnatal PCE exposure (b)]. Black contour bands indicate areas of statistically significant increased or decreased odds of outcomes.The scale includes most, but not all, observed odds ratios.

Geographic distribution of PTSD vs. no reported mental illness from analyses using the optimal span size for each model [unadjusted (a) and adjusted for sex, year of birth, family history of mental health diagnosis, father’s occupation, mother’s educational attainment, maternal smoking during pregnancy, and pre/postnatal PCE exposure (b)]. Black contour bands indicate areas of statistically significant increased or decreased odds of outcomes.The scale includes most, but not all, observed odds ratios.

“Results: We observed spatial variation in the crude odds ratios of depression that was still present even after accounting for spatial confounding due to geographic differences in the distribution of known risk factors (aOR range: 0.61–3.07, P = 0.03). Similar geographic patterns were seen for the crude odds of PTSD; however, these patterns were no longer present in the adjusted analysis (aOR range: 0.49–1.36, P = 0.79), with family history of mental illness most notably influencing the geographic patterns. Analyses of the odds of bipolar disorder did not show any meaningful spatial variation (aOR range: 0.58–1.17, P = 0.82).

“Conclusion: Spatial associations exist between residence at birth and odds of PTSD and depression, but much of this variation can be explained by the geographic distributions of available risk factors. However, these risk factors did not account for all the variation observed with depression, suggesting that other social and environmental factors within our study area need further investigation.”

Geographically Weighted Regression to Measure Spatial Variations in Correlations between Water Pollution versus Land Use in a Coastal Watershed

/ Matt Artz / 1 Comment

OCMOcean & Coastal Management, Volume 103, January 2015, Pages 14–24

By Jinliang Huang, Yaling Huang,Robert Gilmore Pontius Jr., and Zhenyu Zhang

“Highlights

  • GWR reveals spatial variation in water pollution-land use linkages.
  • Water pollution is associated more with built-up than with cropland or forest.
  • More built-up is associated with more pollution for less urbanized sub-watersheds.
  • Forest has a stronger negative association with pollution in urban sub-watersheds.
  • Cropland has a weak association with water pollution among 21 sub-watersheds.

“Land use can influence river pollution and such relationships might or might not vary spatially. Conventional global statistics assume one relationship for the entire study extent, and are not designed to consider whether a relationship varies across space. We used geographically weighted regression to consider whether relationships between land use and water pollution vary spatially across a subtropical coastal watershed of Southeast China. Surface water samples of baseflow for seven pollutants were collected twelve times during 2010–2013 from headwater sub-watersheds. We computed 21 univariate regressions, which consisted of three regressions for each of the seven pollutants. Each of the three regressions considered one of three independent variables, i.e. the percent of the sub-watershed that was cropland, built-up, or forest.

Local R2 values and local parameter estimates for GWR cropland models among three types of sub-watershed.

Local R2 values and local parameter estimates for GWR cropland models among three types of sub-watershed.

“Cropland had a local R2 less than 0.2 for most pollutants, while it had a positive association with water pollution in the agricultural sub-watersheds and a negative association with water pollution in the non-agricultural sub-watersheds. Built-up had a positive association with all pollutants consistently across space, while the increase in pollution per increase in built-up density was largest in the sub-watersheds with low built-up density. The local R2 values were stronger with built-up than with cropland and forest. The local R2 values for built-up varied spatially, and the pattern of the spatial variation was not consistent among the seven pollutants. Forest had a negative association with most pollutants across space. Forest had a stronger negative association with water pollution in the urban sub-watersheds than in the agricultural sub-watersheds. This research provides an insight into land-water linkages, which we discuss with respect to other watersheds in the literature.”

A GIS-based Relational Data Model for Multi-dimensional Representation of River Hydrodynamics and Morphodynamics

/ Matt Artz / 1 Comment

EMS-S13648152Environmental Modelling & Software, Volume 65, March 2015, Pages 79–93

By Dongsu Kim, Marian Muste, and Venkatesh Merwade

“Highlights

  • Represent river data in a curvilinear coordinate system to support river channel oriented spatial analyses.
  • Represent multidimensional river features through points, lines, polygons, and volumes.
  • Represent simulated gridded data for river channels that can be readily coupled with observed data.
  • Represent spatio-temporal evolution of dynamic river objects using Eulerian or Lagrangian observational frameworks.
  • Efficiently store and retrieve data acquired in-situ along with the ancillary metadata.

“The emerging capabilities of the geo-based information systems to integrate spatial and temporal attributes of in-situ measurements is a long-waited solution to efficiently organize, visualize, and analyze the vast amount of data produced by the new generations of river instruments. This paper describes the construct of a river data model linked to a relational database that can be populated with both measured and simulated river data to facilitate descriptions of river features and processes using hydraulic/hydrologic terminology.

Diagram of the connectivity between multidimensional river objects in a cross-section and the river network: Relationship between the CrossSection3DPoint and CrossSection2DPoint in 3D cross-sections.

Diagram of the connectivity between multidimensional river objects in a cross-section and the river network: Relationship between the CrossSection3DPoint and CrossSection2DPoint in 3D cross-sections.

“The proposed model, labeled Arc River, is built in close connection with the existing Arc Hydro data model developed for water-related features to ensure the connection of the river characteristics with their floodplains and watersheds. This paper illustrates Arc River data model capabilities in conjunction with Acoustic Doppler Current Profiler measurements to demonstrate that essential river morphodynamics and hydrodynamics aspects can be described using data on the flow and its boundaries.”