Windmill Site Selection Using Remote Sensing and GIS – A Case Study in Andaman, India

K. Selvavinayagam

“The Andaman and Nicobar Islands are the summits of a submarine mountain range lying on the great tectonic suture zone that extends from the eastern Himalayas to the Arakan along the Myanmar border and finally to Sumatra and lesser Sundaes. This archipelago consists of a group of 572 islands, islets and rock outcrops, but there are a total of 352 important islands comprising the main chain of Andaman and Nicobar, Ritches Archipelago and the out laying volcanic islands of Narcondam and Barren. The islands are spread over an area of 8,249, of which 6,408 sq. km of area is occupied by the Andaman group and 1,841 by the Nicobar groups of Islands. The Andaman group consists of 324 islands of which 24 are inhabited while the Nicobar group includes 28 islands of which 12 are inhabited. Undulating topography and intervening valleys characterize the physiography of this Archiepelago. There are several rain-fed streams, which dry up during summer. All the major islands support a luxuriant growth of evergreen, semi evergreen, moist deciduous and littoral forests from the water edge to the mountain top depending on the topography and nature of the soil. For administrative purposes, the Islands are divided into two districts, namely Andaman and Nicobar. There are a total of 204 revenue villages of which 197 are in the Andaman District. The Andaman and Nicobar is having a good economic turnover through Tourism Industry because of its rich natural scenic beauty and natural resources. At the same time these islands are facing problems such as population growth, commercial development etc and inturn facing acute power shortage.”

Got Results? Spatial Regression or Other Methods for the Analyses of Crime-Social Disorganization Covariates

Paper presented at The American Society of Criminology Annual Meeting, Philadelphia Marriott Downtown, Philadelphia, PA, Nov 04, 2009 . 2010-05-11

MoonSun Kim and Seongmin Park

“It is well known that crime rates are higher in socially disorganized areas. Numerous literature has examined the covariates between crime rates and community variables with such different statistical methods as OLS and HLM. These previous approach, however, has not paid enough attention to address the spatial autocorrelation in their analyses while it might exist due to the proximity of spatial locations of the units.

“This study is designed to examine any differences between spatial regression we will use and other traditional methods by using crme data in a city and census information. Crime specific analyses will be provided with census block group as a unit of analysis.”

Scientists Seeking NSF Funding Will Soon Be Required to Submit Data Management Plans

Government-wide emphasis on community access to data supports substantive push toward more open sharing of research data

During the May 5th meeting of the National Science Board, National Science Foundation (NSF) officials announced a change in the implementation of the existing policy on sharing research data. In particular, on or around October, 2010, NSF is planning to require that all proposals include a data management plan in the form of a two-page supplementary document. The research community will be informed of the specifics of the anticipated changes and the agency’s expectations for the data management plans.

The changes are designed to address trends and needs in the modern era of data-driven science.

“Science is becoming data-intensive and collaborative,” noted Ed Seidel, acting assistant director for NSF’s Mathematical and Physical Sciences directorate. “Researchers from numerous disciplines need to work together to attack complex problems; openly sharing data will pave the way for researchers to communicate and collaborate more effectively.”

“This is the first step in what will be a more comprehensive approach to data policy,” added Cora Marrett, NSF acting deputy director. “It will address the need for data from publicly-funded research to be made public.”

Seidel acknowledged that each discipline has its own culture about data-sharing, and said that NSF wants to avoid a one-size-fits-all approach to the issue. But for all disciplines, the data management plans will be subject to peer review, and the new approach will allow flexibility at the directorate and division levels to tailor implementation as appropriate.

This is a change in the implementation of NSF’s long-standing policy that requires grantees to share their data within a reasonable length of time, so long as the cost is modest.

“The change reflects a move to the Digital Age, where scientific breakthroughs will be powered by advanced computing techniques that help researchers explore and mine datasets,” said Jeannette Wing, assistant director for NSF’s Computer & Information Science & Engineering directorate.  “Digital data are both the products of research and the foundation for new scientific insights and discoveries that drive innovation.”

NSF has a variety of initiatives focused on advancing the vision of data-intensive science. The issue is central to NSF’s Sustainable Digital Data Preservation and Access Network Partners (DataNet) program in the Office of Cyberinfrastructure.

“Twenty-first century scientific inquiry will depend in large part on data exploration,” said José Muñoz, acting director of the Office of Cyberinfrastructure.  “It is imperative that data be made not only as widely available as possible but also accessible to the broad scientific communities.”

Seidel noted that requiring the data management plans was consistent with NSF’s mission and with the growing interest from U.S. policymakers in making sure that any data obtained with federal funds be accessible to the general public. Along with other federal agencies, NSF is subject to the Open Government Directive, an effort of the Obama administration to make government more transparent and more participatory.

[Source: National Science Foundation press release]

Evaluating the Relative Environmental Impact of Countries

PLoS ONE, 01 May 2010, Volume 5, Issue 5, e10440

Corey J. A. Bradshaw, Xingli Giam, and Navjot S. Sodhi

“Environmental protection is critical to maintain ecosystem services essential for human well-being. It is important to be able to rank countries by their environmental impact so that poor performers as well as policy ‘models’ can be identified. We provide novel metrics of country-specific environmental impact ranks – one proportional to total resource availability per country and an absolute (total) measure of impact – that explicitly avoid incorporating confounding human health or economic indicators. Our rankings are based on natural forest loss, habitat conversion, marine captures, fertilizer use, water pollution, carbon emissions and species threat, although many other variables were excluded due to a lack of country-specific data. Of 228 countries considered, 179 (proportional) and 171 (absolute) had sufficient data for correlations. The proportional index ranked Singapore, Korea, Qatar, Kuwait, Japan, Thailand, Bahrain, Malaysia, Philippines and Netherlands as having the highest proportional environmental impact, whereas Brazil, USA, China, Indonesia, Japan, Mexico, India, Russia, Australia and Peru had the highest absolute impact (i.e., total resource use, emissions and species threatened). Proportional and absolute environmental impact ranks were correlated, with mainly Asian countries having both high proportional and absolute impact. Despite weak concordance among the drivers of environmental impact, countries often perform poorly for different reasons. We found no evidence to support the environmental Kuznets curve hypothesis of a non-linear relationship between impact and per capita wealth, although there was a weak reduction in environmental impact as per capita wealth increases. Using structural equation models to account for cross-correlation, we found that increasing wealth was the most important driver of environmental impact. Our results show that the global community not only has to encourage better environmental performance in less-developed countries, especially those in Asia, there is also a requirement to focus on the development of environmentally friendly practices in wealthier countries.”

Government, Media, and GIS Leaders Discuss Gov 2.0

Online Spatial Roundtable Forum Focuses on the Future of Government Services

In the latest discussion, ESRI industry solutions manager Christopher Thomas addresses the emerging trend of governments using Web 2.0 technology to improve service—Gov2.0. Executives from all levels of government, as well as media and geographic information system (GIS) thought leaders, are engaged in a dynamic online conversation as they respond to Thomas’s question: “Can the GIS community provide a platform for engagement that empowers citizens?”

“Government needs to meet high expectation levels,” says Thomas. “Citizens want to interact with their government online, and GIS is playing an important role in delivering these valuable Web services.”

In his post, Thomas discusses the two kinds of Gov 2.0 enthusiasts he sees evolving. One group is more engaged in studying emerging technology, including cloud computing and crowd sourcing, while the other primarily sees Gov 2.0 as a movement to improve government. But a separate group—the largest group, he notes—comprises citizens who are generally unaware of Gov 2.0. In this environment, he sees GIS emerging as a key platform for delivering transparency and accountability that will ultimately help instill more trust in government.

Weigh in on the discussion at

[Source: ESRI press release]

Applying Geostatistical Analysis to Crime Data: Car-Related Thefts in the Baltic States

Geographical Analysis, Volume 42 Issue 1  (January 2010) p 53-77

Ruth Kerry, Pierre Goovaerts, Robert P. Haining, and Vania Ceccato

“Geostatistical methods have rarely been applied to area-level offense data. This article demonstrates their potential for improving the interpretation and understanding of crime patterns using previously analyzed data about car-related thefts for Estonia, Latvia, and Lithuania in 2000. The variogram is used to inform about the scales of variation in offense, social, and economic data. Area-to-area and area-to-point Poisson kriging are used to filter the noise caused by the small number problem. The latter is also used to produce continuous maps of the estimated crime risk (expected number of crimes per 10,000 habitants), thereby reducing the visual bias of large spatial units. In seeking to detect the most likely crime clusters, the uncertainty attached to crime risk estimates is handled through a local cluster analysis using stochastic simulation. Factorial kriging analysis is used to estimate the local- and regional-scale spatial components of the crime risk and explanatory variables. Then regression modeling is used to determine which factors are associated with the risk of car-related theft at different scales.”