Review of Methods for Space-time Disease Surveillance

Spatial and Spatio-temporal Epidemiology, In Press, Accepted Manuscript, Available Online 20 February 2010

Colin Robertsona, Trisalyn A. Nelsona, Ying C. MacNabb and Andrew B. Lawsonc

“A review of some methods for analysis of space-time disease surveillance data is presented. Increasingly, surveillance systems are capturing spatial and temporal data on disease and health outcomes in a variety of public health contexts. A vast and growing suite of methods exists for detection of outbreaks and trends in surveillance data and the selection of appropriate methods in a given surveillance context is not always clear. While most reviews of methods focus on algorithm performance, in practice, a variety of factors determine what methods are appropriate for surveillance. In this review, we focus on the role of contextual factors such as scale, scope, surveillance objective, disease characteristics, and technical issues in relation to commonly used approaches to surveillance. Methods are classified as testing-based or model-based approaches. Reviewing methods in the context of factors other than algorithm performance highlights important aspects of implementing and selecting appropriate disease surveillance methods.”

GIS-based Modeling of Drought and Historical Population Change on the Canadian Prairies

Journal of Historical Geography, Volume 36, Issue 1, January 2010, Pages 43-56

Robert McLemana, Sam Herolda, Zoran Reljica, Mike Sawadaa, and Daniel McKenney”

This article describes the development of a GIS-based model of historical drought and population change in western Canada, designed to support qualitative field research into drought adaptation and migration. The model combines digitized census data and recently available modeled historical climate data at a 10 km2 grid cell scale and can be used to generate maps of ‘hotspots’ where historical declines in rural populations may be associated with extended periods of heat and lack of precipitation. The results suggest a promising avenue for expanding and refining GIS-based modeling of historical human–climate interactions to support qualitative research and to potentially serve as a stepping stone toward forecasting future risk areas of drought-related migration in continental dryland areas.”

Assessing the Context of Health Care Utilization in Ecuador: A Spatial and Multilevel Analysis

BMC Health Services Research, 2010, 10:64

Daniel F Lopez-Cevallos and Chunhuei Chi

“Background: There are few studies that have analyzed the context of health care utilization, particularly in Latin America. This study examines the context of utilization of health services in Ecuador; focusing on the relationship between provision of services and use of both preventive and curative services.

“Methods: This study is cross-sectional and analyzes data from the 2004 National Demographic and Maternal & Child Health dataset. Provider variables come from the Ecuadorian System of Social Indicators (SIISE). Global Moran’s I statistic is used to assess spatial autocorrelation of the provider variables. Multilevel modeling is used for the simultaneous analysis of provision of services at the province level with use of services at the individual level.

“Results: Spatial analysis indicates no significant differences in the density of health care providers among Ecuadorian provinces. After adjusting for various predisposing, enabling, need factors and interaction terms, density of public practice health personnel was positively associated with use of preventive care, particularly among rural households. On the other hand, density of private practice physicians was positively associated with use of curative care, particularly among urban households.

“Conclusions: There are significant public/private, urban/rural gaps in provision of services in Ecuador; which in turn affect people’s use of services. It is necessary to strengthen the public health care delivery system (which includes addressing distribution of health workers) and national health information systems. These efforts could improve access to health care, and inform the civil society and policymakers on the advances of health care reform.”

Natural Earth: Free Vector and Raster Map Data at 1:10m, 1:50m, and 1:110m Scales

“Natural Earth is a public domain map dataset available at 1:10m, 1:50m, and 1:110m scales. Featuring tightly integrated vector and raster data, with Natural Earth you can make a variety of visually pleasing, well-crafted maps with cartography or GIS software.

“Natural Earth was built through a collaboration of many volunteers and is supported by NACIS (North American Cartographic Information Society), and is free for use in any type of project.”

Call for Papers: Workshop On Linked Spatiotemporal Data 2010

Workshop On Linked Spatiotemporal Data 2010 (http://stko.psu.edu/lstd2010/)

In conjunction with the 6th International Conference on Geographic Information Science (GIScience 2010)

Zurich, 14-17th September, 2010; the workshop will be held on the 14th September 2010.

Workshop Description & Scope

Whilst the Web has changed with the advent of the Social Web from mostly authoritative towards increasing amounts of user generated content, it is essentially still about linked documents. These documents provide structure and context for the described data and easy their interpretation. In contrast, the upcoming Data Web is about linking data, not documents. Such data sets are not bound to a specific document but can be easily combined and used outside of the original context. With a growth rate of millions of new facts encoded as RDF-triples per month, the Linked Data cloud allows users to answer complex queries spanning multiple sources. Due to the uncoupling of data from its original creation context, semantic interoperability, identity resolution, and ontologies are central methodologies to ensure consistency and meaningful results. Space and time are fundamental ordering relations to structure such data and provide an implicit context for their interpretation. Prominent geo-related Linked Data hubs include Geonames.org as well as the Linked Geo Data project which provides a RDF serialization of Open Street Map. Furthermore, myriad other Linked Data sources contain location-based references. This workshop aims at introducing the GIScience audience to the Linked Data Web and discuss the relation between the upcoming Linked Data infrastructures and existing OGC services-based Spatial Data Infrastructures. The workshop results will directly contribute to the ongoing work of the NeoGeo Semantic Web Vocabularies Group, an online group focused on the construction of a set of lightweight geospatial ontologies for Linked Data. Overall, the workshop should help to better define the data, knowledge representations, reasoning methodologies, and additional tools needed to link locations seamlessly into the Web of Linked Data. Subsequently, with the advent of “Linked Locations” in Linked Data, the gap between the Semantic Web and the Geo Web will begin to narrow.

Topics of interest for the Linked Spatiotemporal Data workshop include (but are not limited to):

Application of Linked Spatiotemporal Data

  • Linked Data and the Sensor Web Enablement
  • Linked Data and mobile applications
  • Linked Data gazetteers and points of interest
  • Linked Data in the domain of cultural heritage research

Retrieving and Browsing of Linked Spatiotemporal Data

  • Mining Linked Spatiotemporal Data from existing sources
  • Spatiotemporal indexing of Linked Data
  • Harvesting Linked Data from heterogeneous sources
  • Spatial extensions to query languages such as SPARQL (e.g., GeoSPARQL)
  • Visualizing and browsing through the Linked Spatiotemporal Data cloud

Integration and Interoperation of Linked Spatiotemporal Data

  • Ontologies and vocabularies to support interoperability
  • Identity assumptions and resolution for data fusion and integration
  • The role of space and time to structure Linked Data
  • Versioning of spatio-temporal data
  • Semantic annotation and microformats
  • Adding contextual information to Linked Data

Linked Data and Volunteered Geographic Information (VGI)

  • Spatiotemporal Aspects of Data Quality, Trust, and Provenance in Linked Data
  • Tag and Vocabulary recommendations for annotating VGI
  • Maintenance of links

More information

Spatial-temporal Analysis of Gummosis in Three Cashew Clones at Northeastern Brazil

Journal of Phytopathology, Published Online: Mar 11 2010

Alex Q. Cysne, José E. Cardoso, Aline de Holanda N. Maia, and Fabio C. Farias

“The cashew gummosis caused by the fungus Lasiodiplodia theobromae is one of the most important disease of cashew in the northeast of Brazil. The lack of studies about method of early detection, pathogen dissemination, host predisposition, mechanisms of attack and defence and efficient control measures assures this disease as a limiting factor as to growing of cashew under semi-arid conditions. Therefore, the characterization of spatial patterns of gummosis development under commercial orchards may provide important insights into the mechanisms involving in dissemination and disease progress of this disease, as well as in the understanding of dynamic of host, pathogen and environmental interactions for this pathossystem. This work aimed to characterize gummosis temporal and special dynamics in three commercial orchards of cashew clones of cashew with different levels of susceptibility by studying the special arrangement of diseased plants. Disease incidence and severity, quantified determined by a descriptive scale in clones BRS 226 (resistant), Embrapa 51 (slightly resistant) and Faga 11 (susceptible) in a commercial orchard located in Pio IX district (Piaui state, Brazil), were monitored and mapped. Data were collected within three blocks of 90 plants for each clone. Indices of dispersion were estimated to study the spatial dynamic. The dynamics and structure of gummosis foci were also analysed. As expected, data showed different degrees of gummosis incidence and severity for the three clones. Even under different levels of disease, a random dispersion pattern model of dispersion could be observed at the beginning of epidemic for all clones. However, as disease develops, a clustered model is likely to fit. The increase in disease incidence resulted from the increasing in both focus number and size.”