Mapping App Enables Global Dialog around Aid Projects

AidData and Esri Submit Prototype to World Bank’s “Apps for Development” Competition

Public voting closes today, February 28, 2011, for World Bank’s “Apps for Development” competition, which includes an innovative mapping tool created by AidData with support from Esri. AidData’s Development Loop application presents a visual story of assistance, need, and impact driving the complex flow of development funds around the globe. To vote or access a demo and prototype of Development Loop, visit the Apps for Development website.

AidData—a joint initiative of Brigham Young University, the College of William and Mary, and nonprofit organization Development Gateway—captures and interprets development data to support aid activities. Development Loop integrates project data from the World Bank and African Development Bank with success stories, development indicators, and relevant local information on an interactive map. Future enhancements, such as social media integration and free mobile offerings, will facilitate place-based feedback loops among donors, development professionals, and aid recipients.

Through the app challenge, AidData hopes to encourage donor organizations to collaborate in building a comprehensive picture of development efforts and a dynamic platform on which to plan, analyze, and evaluate projects.

“Development Loop goes far beyond depicting where and how funds are being spent,” says Stephen Davenport, senior director for business development at Development Gateway. “It can open the lines of communication and track funding as it moves from one organization to another to the intended beneficiary. It makes projects more empowering and efficient and establishes a platform for sustainable workflows that can be incorporated into existing reporting processes.”

The Development Loop prototype is built on Esri’s ArcGIS API for Microsoft Silverlight/WPF and ArcGIS Server technology. World Bank and African Development Bank activity data was geocoded through the Mapping for Results project, a partnership between AidData and the World Bank.

Organizations interested in integrating project information into Development Loop may contact AidData at info@aiddata.org or Salim Sawaya of Esri at ssawaya@esri.com. To learn more about Esri, visit esri.com. For information on AidData, visit aiddata.org.

[Source: Esri press release]

A Brief History of Spatial Analysis

March 3, 2011, Department of Statistics, University of Illinois

Anil Bera

“Spatial econometrics which is essentially concerned with statistical techniques to take account of economic interactions among agents located on space is relatively a new field of research. However, spatial analysis in general, has a long history. In the statistics literature, R.A. Fisher was probably the first to recognize the implications of spatial dependence. While discussing the shapes of blocks and plots in agricultural experiments, he commented, After choosing the area we usually have no guidance beyond the widely verified fact that patches in close proximity are commonly more alike, as judged by the yield of crops, than those which are further apart, see Fisher (1937, pp.73-74). Even after so many years, the basic tenet of spatial dependence has not changed much from Fishers characterization. In the current statistics and econometrics literature, spatial dependence is also loosely defined as the coincidence of value similarity with location similarity. Every beginning, however, has its own beginning. I will start at the very beginning, and discuss the disconnected developments in spatial analysis during the last century. Then I will move to spatial econometrics, and mention some key developments. Finally, I will try to cover some of my joint work with students/colleagues on specification tests for spatial models.”

Using MODIS Satellite Imagery to Predict Hantavirus Risk

Global Ecology and Biogeography, Article first published online: 17 FEB 2011

Lina Cao, Thomas J. Cova, Philip E. Dennison, and Denise Dearing

“Aims: Sin Nombre virus (SNV), a strain of hantavirus, causes hantavirus pulmonary syndrome (HPS) in humans, a deadly disease with high mortality rate (> 50%). The primary virus host is the deer mouse, and greater abundance of deer mice has been shown to increase the human risk of HPS. Our aim is to identify and compare vegetation indices and associated time lags for predicting hantavirus risk using remotely sensed imagery.

“Location: Utah, USA.

“Methods: A 5-year time-series of moderate-resolution imaging spectroradiometer (MODIS) satellite imagery and corresponding field data was utilized to compare various vegetation indices that measure productivity with the goal of indirectly estimating mouse abundance and SNV prevalence. Relationships between the vegetation indices and deer mouse density, SNV prevalence and the number of infected deer mice at various time lags were examined to assess which indices and associated time lags might be valuable in predicting SNV outbreaks.

“Results: The results reveal varying levels of positive correlation between the vegetation indices and deer mouse density as well as the number of infected deer mice. Among the vegetation indices, the normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI) produced the highest correlations with deer mouse density and the number of infected deer mice using a time lag of 1.0 to 1.3 years for May and June imagery.

“Main conclusions: This study demonstrates the potential for using MODIS time-series satellite imagery in estimating deer mouse abundance and predicting hantavirus risk. The 1-year time lag provides a great opportunity to apply satellite imagery to predict upcoming SNV outbreaks, allowing preventive strategies to be adopted. Analysis of different predictive indices and lags could also be valuable in identifying the time windows for data collection for practical uses in monitoring rodent abundance and subsequent disease risk to humans.”

Representing Places and Change: From the Dynastic to the Diurnal

GIScience Colloquium, University of Zurich-Irchel, May 3, 2011

Paul Longley

“This presentation will take stock of ongoing work at University College London that seeks to profile the dynamics of local area change at temporal scales ranging from the dynastic to the diurnal. We begin with the genetic structure of Great Britain, and evidence that the historic origins of family names remain important in understanding spatial structure. Our regional classification suggests that population mix is today an amalgam of contagious and hierarchical diffusion – with the former incremental over time, and the latter precipitated by episodic events. Next, we reflect on 80 years’ research into the kaleidoscope of change in urban areas, which has culminated in use of geodemographic indicators of social similarity and built form to link disparate geographic locations. Such indicators have more recently been used to characterise the consumption of public as well as private goods, although this begs interesting questions about the geographic scale at which they might best be deployed to characterise neighbourhood expectations and community attitudes. We then consider the impacts of recent technologies that at the same time facilitate change in the way that individuals interact with their (virtual) communities while providing new ways of measuring, monitoring and hence generalising about such interactions. In conclusion we draw these different strands together in order to propose geodemographics that accommodate the unique virtual and conventional demographic characteristics that characterise different locations. We argue that the change dynamics of such indicators say much about social mobility and social interaction in Britain, and internationally.”

Recent Advances in Mobility Data Mining

GIScience Colloquium, University of Zurich-Irchel, April 12, 2011

Prof. Yannis Theodoridis

“A flood of data pertinent to moving objects is available today, and will be more in the near future, particularly due to the automated collection of telecom data from mobile phones and other location-aware devices. Such wealth of data, referenced both in space and time, may enable novel applications of high societal and economic impact, provided that the discovery of consumable and concise knowledge out of these raw data is made possible. In this talk, we will overview recent research activities on mobility data mining and knowledge discovery. Frequent pattern mining, trajectory clustering, outlier detection, sampling, are among the techniques that will be presented. Since data privacy raises concerns, some ideas on privacy-preserving mobility data mining will be also discussed.”

Climate Change Adaptation and Disaster Risk Reduction Institutional and Policy Landscape in Asia and Pacific

Sistema Económico Latinoamericano y del Caribe

December 2010

“The Asia and Pacific region is the world’s most disaster prone. There are a number of disaster risk hotspots in the region, and it is expected that existing risk patterns will intensify as a result of climate change. Responding to these challenges, the Asia and Pacific region has witnessed promising developments to advance disaster risk reduction (DRR) and climate change adaptation (CCA) at regional, sub‐regional, and national levels.

“The DRR and CCA represent policy goals, one concerned with an ongoing problem (disasters) and the other with an emerging issue (climate change). While these concerns have different origins, they overlap a great deal through the common factor of weather and climate and the similar tools used to monitor, analyze and address adverse consequences. It makes sense, therefore, to consider them and implement them in a systematic and integrated manner.

“DRR is the concept and practice of reducing disaster risks through systematic efforts to analyze and manage the causal factors of disasters, including through reduced exposure to hazards, lessened vulnerability of people and property, wise management of land and the environment, and the improved preparedness for adverse events (UNISDR, 2009). CCA means the adjustment in natural or human systems in response to actual or expected climatic stimuli or their effects, which moderates harm or exploits beneficial opportunities (United Nations Framework Convention on Climate Change‐UNFCCC).”

A Temporal Analysis on the Dynamics of Deep-sea Macrofauna: Influence of Environmental Variability off Catalonia Coasts (Western Mediterranean)

Deep Sea Research Part I: Oceanographic Research Papers, available online 5 February 2011

V. Mamouridis, J.E. Cartes, S. Parra, E. Fanelli, and J.I. Saiz Salinas

“A seasonal analysis of deep-sea infauna (macrobenthos) based on quantitative sampling was conducted over the Catalan Sea slope, within the Besòs canyon (atnot, vert, similar550-600 m) and on its adjacent slope (at 800 m). Both sites were sampled in February, April, June-July and October 2007. Environmental variables influencing faunal distribution were also recorded in the sediment and sediment/water interface. Dynamics of macrobenthos at the two stations showed differences in biomass/abundance patterns and trophic structures. Biomass was higher inside the Besòs canyon than on the adjacent slope. The community was mostly dominated by surface-deposit feeding polychaetes (Ampharetidae, Paraonidae, Flabelligeridae) and crustaceans (amphipods such as Carangoliopsis spinulosa and Harpinia spp.) inside the canyon, while subsurface deposit feeders (mainly the sipunculan Onchnesoma steenstrupii) were dominant over the adjacent slope. The taxonomic composition in the suprabenthic assemblages of polychaetes, collected on the adjacent slope by a suprabenthic sledge, was clearly different from that collected by the box-corer. The suprabenthic assemblage was dominated by carnivorous forms (mainly Harmothoe sp. and Nephthys spp.) and linked to higher near-bottom turbidity. Inside Besòs a clear temporal succession of species was related to both food availability and quality and the proliferation of opportunistic species was consistent with higher variability in food sources (TOC, C/N, δ13C) in comparison to adjacent slope. This was likely caused by a greater influence of terrigenous inputs from river discharges. Inside the canyon, Capitellidae, Spionidae and Flabelligeridae, in general considered as deposit-feeders, were more abundant in June-July coinciding with a clear signal of terrigenous carbon (depleted δ13C, high C/N) in the sediments. By contrast, during October and under conditions of high water turbidity and increases of TOM, carnivorous polychaetes (Glyceridae, Onuphidae) increased. Total macrobenthos biomass found over Catalonian slopes, were higher than that found in the neighbouring Toulon canyon, probably because the two canyons are influenced by different river inputs, connected with distinct terrigenous sources.”