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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.”
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.”
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.”
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.”
Epidemiology and Infection, Vol. 137, No. 6 (Jun., 2009), pp. 847-857
S. E. Fenton, H. E. Clough, P. J. Diggle, S. J. Evans, H. C. Davison, W. D. Vink and N. P. French
“Using data from a cohort study conducted by the Veterinary Laboratories Agency (VLA), evidence of spatial clustering at distances up to 30 km was found for S. Agama and S. Dublin (P values of 0·001) and borderline evidence was found for spatial clustering of S. Typhimurium (P = 0·077). The evolution of infection status of study farms over time was modelled using a Markov Chain model with transition probabilities describing changes in status at each of four visits, allowing for the effect of sampling visit. The degree of geographical clustering of infection, having allowed for temporal effects, was assessed by comparing the residual deviance from a model including a measure of recent neighbourhood infection levels with one excluding this variable. The number of cases arising within a defined distance and time period of an index case was higher than expected. This provides evidence for spatial and spatio-temporal clustering, which suggests either a contagious process (e.g. through direct or indirect farm-to-farm transmission) or geographically localized environmental and/or farm factors which increase the risk of infection. The results emphasize the different epidemiology of the three Salmonella serovars investigated.”
Jack Dangermond opens the “Space-Time Modeling and Analysis Workshop” at the inaugural Redlands GIS Week, Monday, 22 February 2010.
Ocean Sci. Discuss., 6, 2831-2859, 2009
Z. T. Botin, L. T. David, R. C. H. del Rosario, and L. Parrott
“A spatio-temporal complexity (STC) measure which has been previously used to analyze data from terrestrial ecosystems is employed to analyse 21 years of remotely sensed sea-surface temperature (SST) data from the Philippines. STC on the Philippine wide SST showed the monsoonal variability of the Philippine waters but did not show significant differences between El Niño, La Niña and normal years. The spatial domain was subsequently divided into six thermal regions computed via clustering of temperature means. The STC values of each thermal region showed variations corresponding to the monsoonal shifts – as well as – to ENSO events. STC characterized environmental heterogeneity over space and time has the potential to define limits of bio-regions. The same approach can be utilized for many long-term remotely sensed data.”
Theoretical and Applied Climatology, January 2010
K. Cindrić, Z. Pasarić, and M. Gajić-Čapka
“Systematic statistical analysis of dry day sequences, which are defined according to 0.1, 1, 5 and 10 mm of precipitation-per-day thresholds, is performed on seasonal and yearly basis. The data analysed come from 25 Croatian meteorological stations and cover the period 1961–2000. Climatological features of the mean and maximum dry spell durations, as well as the frequency of long dry spells (>20 days) are discussed. The results affirm the three main climatological regions in Croatia, with the highlands exhibiting shorter dry spells than the mainland, and the coastal region exhibiting longer dry spells. The prevailing positive trend of both mean and maximal durations is detected during winter and spring seasons, while negative trend dominate in autumn for all thresholds. Positive field significant trends of mean dry spell duration with 5 and 10 mm thresholds are found during spring and the same is valid for annual maximum dry spell duration with a 10 mm threshold. It is found that the Discrete Autoregressive Moving Average (DARMA(1,1)) model can be used to estimate the probabilities of dry spells in Croatia that are up to 20–30 days long.”
Environmental Modelling & Software, Volume 24, Issue 9, September 2009, Pages 1088-1099
O. Schmitz, D. Karssenberg, W.P.A. van Deursen, and C.G. Wesseling
“An important step in the procedure of building an environmental model is the transformation of a conceptual model into a numerical simulation. To simplify model construction a framework is required that relieves the model developer from software engineering concerns. In addition, as the demand for a holistic understanding of environmental systems increases, access to external model components is necessary in order to construct integrated models.
“We present a modelling framework that provides two- and three-dimensional building blocks for construction of spatio-temporal models. Two different modelling languages available in the framework, the first tailored and the second an enhanced Python scripting language, allow the development and modification of models. We explain for both languages the interfaces allowing to link specialised model components and thus extending the functionality of the framework. We demonstrate the coupling of external components in order to create multicomponent models by the development of the link to the groundwater model MODFLOW and provide results of an integrated catchment model. The approach described is appropriate for constructing integrated models that include a coupling of a small number of components.”
