Smart Grid Study Identifies Utility Hurdles

U.S. utilities must correct, update, and integrate customer and infrastructure data before a smart grid can be effectively implemented, according to a benchmark study conducted by Esri. The smart grid, a popular plan to add communication and computer technology to electric networks, promises to make energy cleaner and more reliable.

Of the 226 study respondents, 71 percent view geographic information system (GIS) technology as strategic to the smart grid; the remaining 29 percent believe GIS plays a significant role. According to the study, utilities report a lag time of up to 90 days to move data from the field into the GIS. Data accuracy is reportedly spotty, and data is either incomplete or not GPS accurate.

Utility operators will need GIS for crucial smart grid requirements such as collecting and updating data and managing the installation of smart meters and sensors. GIS is also seen as a critical tool for analyzing energy consumption and incorporating renewable energy resources.

“Simply put, GIS facilitates the building and operation of a smart grid,” said Bill Meehan, utility solutions director for GIS software company Esri. “However, many utilities in the United States acknowledge that the data in their GIS is not ready for the smart grid. Without accurate data and GIS for monitoring things like demand response and consumer behavior, the smart grid may not live up to its lofty expectations.”

To view study results, watch the video, and learn more about the role of GIS in the smart grid, visit

[Source: Esri press release]

Estimating Sub-pixel to Regional Winter Crop Areas using Neural Nets

ISPRS Technical Commission VII Symposium: 100 Years ISPRS – Advancing Remote Sensing Science, 05-07 July 2010, Vienna, Austria

Clement Atzberger, and Felix Rembold

“The work aimed at testing a methodology which can be applied to low spatial resolution satellite data to assess inter-annual crop area variations on sub-pixel to regional scales. The methodology is based on the assumption that within mixed pixels land cover variations are reflected by changes in the related hyper-temporal profiles of the Normalised Difference Vegetation Index (NDVI). We evaluated if changes in the fractional winter crop coverage are reflected in changing shapes of annual NDVI profiles and can be detected by using neural networks. The neural nets were trained on reference data obtained from high resolution Landsat TM/ETM images. The proposed methodology was applied in a study region in central Italy to estimate winter crop areas between 1988 and 2002 from 1 km resolution NOAA-AVHRR profiles and additional ancillary data readily available (CORINE land cover). The accuracy of the estimates was assessed by comparison to official agricultural statistics using a bootstrap approach. The method showed promise for estimating crop area variation on sub-pixel level (cross-validated R2 between 0.7 and 0.8) to regional scales (normalized RMSE: 10%). The network based approach proved to have a significantly higher forecast capability than other methods used previously for the same study area.”

An Examination of Job Titles Used for GIScience Professionals

International Journal of Applied Geospatial Research, Vol. 1, Issue 1, 2010

Thomas Wikle

“Over the last 20 years the increasing availability, utility, and importance of geospatial data has led to new kinds of positions in government agencies, non-profit organizations and private businesses. While employment opportunities have grown substantially, guidelines for assigning titles, qualifications and responsibilities have not kept pace. Inconsistency in GIScience titles and job qualifications has made it difficult for employers and job seekers to compare positions across organizations in terms of duties, compensation, and status. This paper explores job titles used by public and private organizations that hire GIScience professionals. An analysis of 204 online job ads shows variation in qualifications associated with titles. Model job titles and career ladders are suggested as a means of attracting and retaining GIScience professionals.”

A Geographical Information System-based Analysis of Cancer Mortality and Population Exposure to Coal Mining Activities in West Virginia, United States of America

Geospatial Health, Volume 4, Number 2, May 2010, Pages 243-256

Michael Hendryx,  Evan Fedorko,  Andrew Anesetti-Rothermel

“Cancer incidence and mortality rates are high in West Virginia compared to the rest of the United States of America. Previous research has suggested that exposure to activities of the coal mining industry may contribute to elevated cancer mortality, although exposure measures have been limited. This study tests alternative specifications of exposure to mining activity to determine whether a measure based on location of mines, processing plants, coal slurry impoundments and underground slurry injection sites relative to population levels is superior to a previously-reported measure of exposure based on tons mined at the county level, in the prediction of age-adjusted cancer mortality rates. To this end, we utilize two geographical information system (GIS) techniques – exploratory spatial data analysis and inverse distance mapping – to construct new statistical analyses. Total, respiratory and “other” age-adjusted cancer mortality rates in West Virginia were found to be more highly associated with the GIS-exposure measure than the tonnage measure, before and after statistical control for smoking rates. The superior performance of the GIS measure, based on where people in the state live relative to mining activity, suggests that activities of the industry contribute to cancer mortality. Further confirmation of observed phenomena is necessary with person-level studies, but the results add to the body of evidence that coal mining poses environmental risks to population health in West Virginia.”

Bringing Geography to the Practice of Analyzing Crime Through Technology

National Institute of Justice discussion paper NCJ 230757, June 2010

Ronald Wilson and Timothy Brown

“In 1997, NIJ established the Crime Mapping Research Center (CMRC), with a focus on using geographic information systems to visualize crime data and understand spatial patterns of criminal activity. CMRC’s efforts were intended to enhance crime analysis by State and local law enforcement and other criminal justice organizations. In 2002, NIJ transformed CMRC into the Mapping and Analysis for Public Safety (MAPS) program. This program focuses on integrating spatial statistics into the measurement of geographic crime patterns. When the program was expanded into NIJ’s Office of Science and Technology (OST), it began examining emerging technologies that would be key tools in crime analysis. Much of what the MAPS program does is called “crime mapping,” which involves more than plotting crime locations. Crime mapping is usually coupled with the use of a geographic information system (GIS), which is a tool for visualizing and manipulating geographic data used to prepare data for statistical analysis, as well as to display the output from analysis. The current use of spatial analysis in the study of crime has been aided by the development of computer GIS software, which is a dominant tool for analyzing crime data. Over the past few years, the MAPS program has funded several geospatial technology research projects intended to advance the collection and geographical analysis of crime data. Four of these projects are briefly described in this report. The report concludes with suggestions for future research and technology related to spatial analysis.”