Comparing Landcover Patterns in Tokyo, Kyoto, and Taipei using ALOS Multispectral Images

Landscape and Urban Planning, 2010

Wei-Chun Hung, Yen-Ching Chen, and Ke-Sheng Cheng

“Understanding the landcover pattern in a region is essential for landuse planning and resources management. In this study ALOS multispectral images were used to compare landcover patterns in three study areas, namely Tokyo, Kyoto, and Taipei, of different degrees of urbanization. From the results of landuse/landcover classification, Shannon diversity index at cell level was used for landcover pattern analysis. Existing landcover pattern of the three study areas were also compared by investigating cell distribution in a landcover coverage-ratio space. Both the landcover type richness and evenness are low in the Tokyo study area and built-up is the single dominant landcover type in almost all cells. In comparison, landcover patterns of the Kyoto and Taipei study areas are more diversified, with significant amount of cells having mixed and non-dominant landcover types. Kyoto is least urbanized and enjoys a good mixture of different landcover types. It was found that cell-average NDVI alone can be used for delineating areas of certain dominant landcover types. Implementation of such method does not require an a priori LULC classification, and thus is particularly useful when good training data for LULC classification are not available. An urbanization index which integrates the coverage ratio of built-up landcover type and the cell-average NDVI was proposed and used to explore the spatial variation of degree of urbanization. Area-average urbanization indices of the Tokyo, Kyoto, and Taipei study areas were calculated to be 0.91, 0.55, and 0.72, respectively. Such results are consistent with the results of qualitative evaluation using different landscape metrics.”

17th Annual California GIS Conference: “Meeting California’s Challenges”, 28-31 March 2011

“The 17th Annual California GIS Conference will take place March 28-31, 2011 in Fresno, California. The conference program, themed “Meeting California’s Challenges” will be developed through a Call for Presentations.

“Until July 31, the conference is offering super-early registration discounts. The discounted 2011 conference registration fee is only $99 until that date.”

Predictive Spatial Analysis of Marine Mammal Habitats

Report Number A326025, January 2010, 296 pages

Andrew Read; Patrick Halpin; Benjamin Best; Ei Fujioka; Caroline Good; Lucie Hazen; Erin LaBrecque; Song Qian; Robert Schick; Duke University Beaufort NC Marine Lab

“We developed a data management, statistical modeling and decision support system describing habitat use of marine mammals in the North Atlantic and Gulf of Mexico. Our objective was to make this information available in a comprehensive manner to environmental planners and decision makers in the Navy and elsewhere. The system uses data on the distribution of marine mammals from dedicated surveys contained in the online OBIS-SEAMAP marine data archive. We used these data to develop predictive habitat models for guilds of marine mammals in these two regions. We delivered model outputs in an online, flexible Spatial Decision Support System (SDSS). The SDSS is a browser-based, interactive mapping application that enables users to view model results with original survey effort and marine mammal observations. In total, we generated 33 models, representing 16 cetacean guilds, using environmental data from the JPL physical oceanographic data archive. Predictive maps for the likelihood of encounter with marine mammals comprise the results, along with estimates of standard errors.”

A General Parallelization Strategy for Random Path Based Geostatistical Simulation Methods

Computers & Geosciences, Volume 36 , Issue 7, July 2010, Pages 953-958

Grégoire Mariethoz

“The size of simulation grids used for numerical models has increased by many orders of magnitude in the past years, and this trend is likely to continue. Efficient pixel-based geostatistical simulation algorithms have been developed, but for very large grids and complex spatial models, the computational burden remains heavy. As cluster computers become widely available, using parallel strategies is a natural step for increasing the usable grid size and the complexity of the models. These strategies must profit from of the possibilities offered by machines with a large number of processors. On such machines, the bottleneck is often the communication time between processors. We present a strategy distributing grid nodes among all available processors while minimizing communication and latency times. It consists in centralizing the simulation on a master processor that calls other slave processors as if they were functions simulating one node every time. The key is to decouple the sending and the receiving operations to avoid synchronization. Centralization allows having a conflict management system ensuring that nodes being simulated simultaneously do not interfere in terms of neighborhood. The strategy is computationally efficient and is versatile enough to be applicable to all random path based simulation methods.”

Human Helminth Co-Infection: Analysis of Spatial Patterns and Risk Factors in a Brazilian Community

PLoS Neglected Tropical Diseases: 2(12), 2008

Rachel L. Pullan1, Jeffrey M. Bethony, Stefan M. Geiger, Bonnie Cundill, Rodrigo Correa-Oliveira, Rupert J. Quinnell, and Simon Brooker

“Background: Individuals living in areas endemic for helminths are commonly infected with multiple species. Despite increasing emphasis given to the potential health impacts of polyparasitism, few studies have investigated the relative importance of household and environmental factors on the risk of helminth co-infection. Here, we present an investigation of exposure-related risk factors as sources of heterogeneity in the distribution of co-infection with Necator americanus and Schistosoma mansoni in a region of southeastern Brazil.

“Methodology: Cross-sectional parasitological and socio-economic data from a community-based household survey were combined with remotely sensed environmental data using a geographical information system. Geo-statistical methods were used to explore patterns of mono- and co-infection with N. americanus and S. mansoni in the region. Bayesian hierarchical models were then developed to identify risk factors for mono- and co-infection in relation to community-based survey data to assess their roles in explaining observed heterogeneity in mono and co-infection with these two helminth species.

“Principal Findings: The majority of individuals had N. americanus (71.1%) and/or S. mansoni (50.3%) infection; 41.0% of individuals were co-infected with both helminths. Prevalence of co-infection with these two species varied substantially across the study area, and there was strong evidence of household clustering. Hierarchical multinomial models demonstrated that relative socio-economic status, household crowding, living in the eastern watershed and high Normalized Difference Vegetation Index (NDVI) were significantly associated with N. americanus and S. mansoni co-infection. These risk factors could, however, only account for an estimated 32% of variability between households.

“Conclusions: Our results demonstrate that variability in risk of N. americanus and S. mansoni co-infection between households cannot be entirely explained by exposure-related risk factors, emphasizing the possible role of other household factors in the heterogeneous distribution of helminth co-infection. Untangling the relative contribution of intrinsic host factors from household and environmental determinants therefore remains critical to our understanding of helminth epidemiology.”