Spatial Analysis of Bladder, Kidney, and Pancreatic Cancer on Upper Cape Cod: An Application of Generalized Additive Models to Case-control Data

Environmental Health, Volume 8, Number 1, 1-13, 10 February 2009

Verónica Vieira, Thomas Webster, Janice Weinberg, and Ann Aschengrau

“Background: In 1988, elevated cancer incidence in upper Cape Cod, Massachusetts prompted a large epidemiological study of nine cancers to investigate possible environmental risk factors. Positive associations were observed, but explained only a portion of the excess cancer incidence. This case-control study provided detailed information on individual-level covariates and residential history that can be spatially analyzed using generalized additive models (GAMs) and geographical information systems (GIS).

“Methods: We investigated the association between residence and bladder, kidney, and pancreatic cancer on upper Cape Cod. We estimated adjusted odds ratios using GAMs, smoothing on location. A 40-year residential history allowed for latency restrictions. We mapped spatially continuous odds ratios using GIS and identified statistically significant clusters using permutation tests.

“Results: Maps of bladder cancer are essentially flat ignoring latency, but show a statistically significant hot spot near known Massachusetts Military Reservation (MMR) groundwater plumes when 15 years latency is assumed. The kidney cancer map shows significantly increased ORs in the south of the study area and decreased ORs in the north.

“Conclusion: Spatial epidemiology using individual level data from population-based studies addresses many methodological criticisms of cluster studies and generates new exposure hypotheses. Our results provide evidence for spatial clustering of bladder cancer near MMR plumes that suggest further investigation using detailed exposure modeling.”

Hotspot Analysis of Spatial Environmental Pollutants Using Kernel Density Estimation and Geostatistical Techniques

International Journal of Environmental Research and Public Health 2011, 8(1), 75-88

Yu-Pin Lin, Hone-Jay Chu, Chen-Fa Wu, Tsun-Kuo Chang, and Chiu-Yang Chen

“Concentrations of four heavy metals (Cr, Cu, Ni, and Zn) were measured at 1,082 sampling sites in Changhua county of central Taiwan. A hazard zone is defined in the study as a place where the content of each heavy metal exceeds the corresponding control standard. This study examines the use of spatial analysis for identifying multiple soil pollution hotspots in the study area. In a preliminary investigation, kernel density estimation (KDE) was a technique used for hotspot analysis of soil pollution from a set of observed occurrences of hazards. In addition, the study estimates the hazardous probability of each heavy metal using geostatistical techniques such as the sequential indicator simulation (SIS) and indicator kriging (IK). Results show that there are multiple hotspots for these four heavy metals and they are strongly correlated to the locations of industrial plants and irrigation systems in the study area. Moreover, the pollution hotspots detected using the KDE are the almost same to those estimated using IK or SIS. Soil pollution hotspots and polluted sampling densities are clearly defined using the KDE approach based on contaminated point data. Furthermore, the risk of hazards is explored by these techniques such as KDE and geostatistical approaches and the hotspot areas are captured without requiring exhaustive sampling anywhere.”

Social Media and Meta-Networks for Crisis Mapping: Collaboratively Building Spatial Data for Situation Awareness in Disaster Response and Recovery Management

Spatio-Temporal Constraints on Social Networks Workshop, University of California, Santa Barbara, Center for Spatial Studies, 13-14 December 2010

Connie White

“After a disaster strikes, answers to questions like, “Who is hurt?” and “Where is the hardest hit area?” may not be so easy to answer. Either while ongoing or after a disaster has occurred, too often the worst hit areas are not detected quickly enough. Due to a lack of awareness, this poses an even greater problem in rural or in the more isolated areas where communications have either failed, worked intermittently on a good day or are absent. No databases may exist providing geographic data on these more isolated or rural areas. The two earlier questions identify the problems this position paper tackles: (1) how do you identify impact zones and then once identified, (2) how do you populate a database with spatial data? Disaster assessment and situational awareness is required to support response and recovery efforts. Locals need to know where to go for help and responders need to know where to go to help those in need. A common site can be created for all stakeholders proving access to a map where anyone can use and access the map anytime and anyone can add information to the map anytime.”