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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.”
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.”
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
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.”
Canadian Geographer / Le Géographe canadien, Volume 54 Issue 1, Pages 29 – 45, March 2010
NADINE SCHUURMAN, MYRIAM BÉRUBÉ, and VALORIE A. CROOKS
“Ensuring equity of access to primary health care (PHC) across Canada is a continuing challenge, especially in rural and remote regions. Despite considerable attention recently by the World Health Organization, Health Canada and other health policy bodies, there has been no nation-wide study of potential (versus realized) spatial access to PHC. This knowledge gap is partly attributable to the difficulty of conducting the analysis required to accurately measure and represent spatial access to PHC. The traditional epidemiological method uses a simple ratio of PHC physicians to the denominator population to measure geographical access. We argue, however, that this measure fails to capture relative access. For instance, a person who lives 90 minutes from the nearest PHC physician is unlikely to be as well cared for as the individual who lives more proximate and potentially has a range of choice with respect to PHC providers. In this article, we discuss spatial analytical techniques to measure potential spatial access. We consider the relative merits of kernel density estimation and a gravity model. Ultimately, a modified version of the gravity model is developed for this article and used to calculate potential spatial access to PHC physicians in the Canadian province of Nova Scotia. This model incorporates a distance decay function that better represents relative spatial access to PHC. The results of the modified gravity model demonstrate greater nuance with respect to potential access scores. While variability in access to PHC physicians across the test province of Nova Scotia is evident, the gravity model better accounts for real access by assuming that people can travel across artificial census boundaries. We argue that this is an important innovation in measuring potential spatial access to PHC physicians in Canada. It contributes more broadly to assessing the success of policy mandates to enhance the equitability of PHC provisioning in Canadian provinces.”
“Biodiverse is a tool for the spatial analysis of diversity using indices based on taxonomic, phylogenetic and matrix-based (e.g. genetic distance) relationships, as well as related environmental and temporal variations.
“Biodiverse supports four processes:
- linked visualisation of data distributions in geographic, taxonomic, phylogenetic and matrix spaces;
- spatial moving window analyses including richness, endemism, phylogenetic diversity and beta diversity;
- spatially constrained agglomerative cluster analyses; and
- randomisations for hypothesis testing.”
Pediatric Blood & Cancer, Volume 54 Issue 4, Pages 511 – 518, 2010
Raid Amin, PhD, Alexander Bohnert, Laurens Holmes, PhD, DrPH, Ayyappan Rajasekaran, PhD, and Chatchawin Assanasen, MD
“Background: Childhood cancer remains the leading cause of disease-related mortality for children. Whereas, improvement in care has dramatically increased survival, the risk factors remain to be fully understood. The increasing incidence of childhood cancer in Florida may be associated with possible cancer clusters. We aimed, in this study, to identify and confirm possible childhood cancer clusters and their subtypes in the state of Florida.
“Methods: We conducted purely spatial and space-time analyzes to assess any evidence of childhood malignancy clusters in the state of Florida using SaTScanTM. Data from the Florida Association of Pediatric Tumor Programs (FAPTP) for the period 2000-2007 were used in this analysis.
“Results: In the purely spatial analysis, the relative risks (RR) of overall childhood cancer persisted after controlling for confounding factors in south Florida (SF) (RR = 1.36, P = 0.001) and northeastern Florida (NEF) (RR = 1.30, P = 0.01). Likewise, in the space-time analysis, there was a statistically significant increase in cancer rates in SF (RR = 1.52, P = 0.001) between 2006 and 2007. The purely spatial analysis of the cancer subtypes indicated a statistically significant increase in the rate of leukemia and brain/CNS cancers in both SF and NEF, P < 0.05. The space-time analysis indicated a statistically significant sizable increase in brain/CNS tumors (RR = 2.25, P = 0.02) for 2006-2007.
“Conclusions: There is evidence of spatial and space-time childhood cancer clustering in SF and NEF. This evidence is suggestive of the presence of possible predisposing factors in these cluster regions. Therefore, further study is needed to investigate these potential risk factors.”
…from the Social Science Research Network…
GATE Working Paper 09-29, December 2009
Florence Goffette-Nagot, Isabelle Reginster, and Isabelle Thomas
“This paper explores the spatial variation of land prices in Belgium. The originality of the methodology is threefold : (1) to work at the spatial extent of an entire country, (2) to compute several accessibility measures to all jobs and several representations of the environmental amenities and, more importantly, (3) to test the hypothesis that jobs influence land prices only in the same linguistic region. Spatial autocorrelation is accounted for by estimating spatial models. The results show that the linguistic border acts as a strong barrier in the spatial pattern of land prices and that environmental variables have no significant effect at this scale of spatial analysis.”
Annals of Epidemiology, Volume 19, Issue 12, Pages 900-907 (December 2009)
Wenbiao Hu, Kerrie Mengersen, and Shilu Tong
“This study explored the spatial distribution of notified cryptosporidiosis cases and identified major socioeconomic factors associated with the transmission of cryptosporidiosis in Brisbane, Australia.
“We obtained the computerized data sets on the notified cryptosporidiosis cases and their key socioeconomic factors by statistical local area (SLA) in Brisbane for the period of 1996 to 2004 from the Queensland Department of Health and Australian Bureau of Statistics, respectively. We used spatial empirical Bayes rates smoothing to estimate the spatial distribution of cryptosporidiosis cases. A spatial classification and regression tree (CART) model was developed to explore the relationship between socioeconomic factors and the incidence rates of cryptosporidiosis.
“Spatial empirical Bayes analysis reveals that the cryptosporidiosis infections were primarily concentrated in the northwest and southeast of Brisbane. A spatial CART model shows that the relative risk for cryptosporidiosis transmission was 2.4 when the value of the social economic index for areas (SEIFA) was over 1028 and the proportion of residents with low educational attainment in an SLA exceeded 8.8%.
“There was remarkable variation in spatial distribution of cryptosporidiosis infections in Brisbane. Spatial pattern of cryptosporidiosis seems to be associated with SEIFA and the proportion of residents with low education attainment.”
Applied Spatial Analysis and Policy, Volume 3, Number 1 / March 2010
Gustavo Garcia Manzato and Antônio Nélson Rodrigues da Silva
“The objective of this exploratory study is to present a new method for monitoring the dynamic changes of functional urban regions (FURs) or metropolitan areas (MAs) boundaries throughout time. The suggested approach is based on two elements: the population density and an index of transportation infrastructure supply, which are analyzed in two ways. First, we carry out exploratory analyses of those variables separately. Next, the variables are combined using spatial analysis and spatial modeling techniques. A case study in the state of São Paulo, Brazil, shows that the proposed methodology can be particularly useful for urban and regional planning in developing countries, because it stresses the relationship between land-use and transportation supply. So, given the evidence that urban and regional development is strongly influenced by the level of transportation infrastructure supply, the approach can be further improved if considering other elements of transportation infrastructure, such as airports, railways, ports, as well as additional factors which may have effects on land use patterns such as distribution of services and jobs where data is available.”
