Renewable Energy Atlas Shows Vermont’s Energy Options

Green Map Powered by ESRI Is Launched on Earth Day

The Renewable Energy Atlas of Vermont, built on ESRI’s ArcGIS technology, was launched on Earth Day, April 22, 2010. Web site visitors can identify, visualize, and analyze data about existing and promising renewable energy projects for Vermont’s towns and counties.

Visitors to the Renewable Energy Atlas of Vermont Web site can select from biomass, efficiency, geothermal, hydroelectric, solar, and wind renewable energy data layers and use geographic information system (GIS) tools to view existing and proposed projects by area. All renewable energy data layers were created and analyzed using ESRI’s ArcGIS Desktop software and published with ArcGIS Server.

The Renewable Energy Atlas of Vermont will assist town energy committees, funders, educators, planners, policy makers, and businesses in making informed decisions about renewable energies in their communities, decisions that ultimately lead to successful projects, greater energy security, a cleaner and healthier environment, and better quality of life across the state.

The Vermont Sustainable Jobs Fund, Vermont Center for Geographic Information, Fountains Spatial, and Overit Media collaborated to develop the atlas in hopes of moving the state’s renewable energy projects from concept to reality. ESRI provided professional service support.

“We were looking for a way to show people how renewable energy flows through their communities so they can see the options for harnessing it,” said Scott Sawyer, research, evaluation, and communications coordinator at Vermont Sustainable Jobs Fund. “The Web site lets them zoom in to a town level or subtown level and look at the renewable energy and efficiency possibility. Our goal was to make the Web site easy and fun. GIS technology, good data, Web design, and easy-to-use applications made this possible.”

The suite of renewable energy options is further broken down into 20 categories of data for specific renewable energy analysis. For example, users can select options for a town, biodiesel, and vegetable oil and see all the restaurants where they can potentially find vegetable oil waste that could be useful for making biodiesel fuel. The site also provides tools for calculating energy potential. A solar model, for instance, allows users to enter a roof’s facing direction, tilt, and percentage of tree shade and produce a calculation of solar energy potential.

The basemaps for the Web site have a beautiful cartographic look. These were acquired from ESRI’s ArcGIS Online basemap services (World Street Map and World Imagery).

“We are excited to work on the project that supports Vermont’s green economy,” noted Mark Haberle, senior project manager at Fountains Spatial. “ESRI’s GIS technology enables the development of tools for robust information discovery and dissemination that makes it easy for people to understand the possibilities of renewable energy. It is our hope that the Renewable Energy Atlas of Vermont will support sustainable job development for Vermont and help the state meet its vision for a carbon-constrained future.”

“GIS technology provides a means for citizens to become informed about their communities and actively participate in making them environmentally sustainable,” said Robin Smith, ESRI’s environmental solutions manager. “ESRI is encouraged by Vermont’s use of geospatial technology to help its citizens participate in the state’s renewable energy efforts.”

Learn about developing a renewable energy Web site for your community by contacting Haberle at Learn more about ESRI’s GIS solutions for the environment at

[Source: ESRI press release]

China’s Ministry of Environmental Protection Deploys GIS for Pollution Source Monitoring

Environmental Observer, Spring 2010

“The Ministry of Environmental Protection of the People’s Republic of China is using GIS to reduce pollutant discharge. China’s manufacturing boom has put its environmental practices in the spotlight of national and international attention.

“Ministry staff members are using ESRI’s ArcGIS software to manage, inventory, and analyze nearly 10,000 pollutant discharge points within the country. Data collection for such a large scope was a challenge faced by GIS project managers when planning the project. With ArcGIS in place, they can integrate GIS and the department’s automated monitoring system as a method for quickly accessing environmental data. GIS supports comparative spatial analysis for conducting side-by-side comparisons of different provinces and different cities.”

Addressing Issues in Sparseness, Ecological Bias and Formulation of the Adjacency Matrix in Bayesian Spatio-temporal Analysis of Disease Counts

Arul Earnest, PhD thesis, 2010

“The main objective of this PhD was to further develop Bayesian spatio-temporal models (specifically the Conditional Autoregressive (CAR) class of models), for the analysis of sparse disease outcomes such as birth defects. The motivation for the thesis arose from problems encountered when analyzing a large birth defect registry in New South Wales. The specific components and related research objectives of the thesis were developed from gaps in the literature on current formulations of the CAR model, and health service planning requirements. Data from a large probabilistically-linked database from 1990 to 2004, consisting of fields from two separate registries: the Birth Defect Registry (BDR) and Midwives Data Collection (MDC) were used in the analyses in this thesis. The main objective was split into smaller goals. The first goal was to determine how the specification of the neighbourhood weight matrix will affect the smoothing properties of the CAR model, and this is the focus of chapter 6. Secondly, I hoped to evaluate the usefulness of incorporating a zero-inflated Poisson (ZIP) component as well as a shared-component model in terms of modeling a sparse outcome, and this is carried out in chapter 7. The third goal was to identify optimal sampling and sample size schemes designed to select individual level data for a hybrid ecological spatial model, and this is done in chapter 8. Finally, I wanted to put together the earlier improvements to the CAR model, and along with demographic projections, provide forecasts for birth defects at the SLA level. Chapter 9 describes how this is done. For the first objective, I examined a series of neighbourhood weight matrices, and showed how smoothing the relative risk estimates according to similarity by an important covariate (i.e. maternal age) helped improve the model’s ability to recover the underlying risk, as compared to the traditional adjacency (specifically the Queen) method of applying weights. Next, to address the sparseness and excess zeros commonly encountered in the analysis of rare outcomes such as birth defects, I compared a few models, including an extension of the usual Poisson model to encompass excess zeros in the data. This was achieved via a mixture model, which also encompassed the shared component model to improve on the estimation of sparse counts through borrowing strength across a shared component (e.g. latent risk factor/s) with the referent outcome (caesarean section was used in this example). Using the Deviance Information Criteria (DIC), I showed how the proposed model performed better than the usual models, but only when both outcomes shared a strong spatial correlation. The next objective involved identifying the optimal sampling and sample size strategy for incorporating individual-level data with areal covariates in a hybrid study design. I performed extensive simulation studies, evaluating thirteen different sampling schemes along with variations in sample size. This was done in the context of an ecological regression model that incorporated spatial correlation in the outcomes, as well as accommodating both individual and areal measures of covariates. Using the Average Mean Squared Error (AMSE), I showed how a simple random sample of 20% of the SLAs, followed by selecting all cases in the SLAs chosen, along with an equal number of controls, provided the lowest AMSE. The final objective involved combining the improved spatio-temporal CAR model with population (i.e. women) forecasts, to provide 30-year annual estimates of birth defects at the Statistical Local Area (SLA) level in New South Wales, Australia. The projections were illustrated using sixteen different SLAs, representing the various areal measures of socio-economic status and remoteness. A sensitivity analysis of the assumptions used in the projection was also undertaken. By the end of the thesis, I will show how challenges in the spatial analysis of rare diseases such as birth defects can be addressed, by specifically formulating the neighbourhood weight matrix to smooth according to a key covariate (i.e. maternal age), incorporating a ZIP component to model excess zeros in outcomes and borrowing strength from a referent outcome (i.e. caesarean counts). An efficient strategy to sample individual-level data and sample size considerations for rare disease will also be presented. Finally, projections in birth defect categories at the SLA level will be made.”

Geospatial Technology and Our Shared Responsibility

Geospatial technologies inspire and enable people to make a positive impact on their future by developing a comprehensive understanding of the changing world around them.  Industry leaders, academics, non-governmental organizations (NGOs), and municipalities rely on GIS to connect them with the analytical knowledge they need to make the critical decisions that shape our planet.

For several decades, the geospatial community has collaborated around a shared commitment to solving global challenges with geographic knowledge, expertise, and stewardship.  This commitment includes:

  • A deeper, geographic understanding of the world.
  • The belief that geography can be used to shape a more resilient and sustainable future.
  • Dedication to solving Earth’s most pressing issues with geographic expertise and rational resolve.
  • An acknowledgement that we are responsible for Earth’s environmental and social resources.
  • A collaborative spirit that connects decision-makers with solutions that shape our future at every scale.

This shared responsibility is our opportunity to use our geographic expertise and rational resolve to make critical decisions concerning our natural and cultural resources.  From community issues to regional concerns and global challenges, geospatial technologies are at the heart of a more resilient and sustainable future. We work today for a better tomorrow.

Happy Earth Day!

Mastering Map Scale: Balancing Workloads using Display and Geometry Change in Multi-scale Mapping

GeoInformatica, Volume 14, Number 2 / April, 2010

Cynthia A. Brewer and Barbara P. Buttenfield

“This paper builds on a body of European research on multiple resolution data bases (MRDBs), defining a conceptual framework for managing tasks in a multi-scale mapping project. The framework establishes a workload incorporating task difficulty, time to complete a task, required level of expertise, required resources, etc. Project managers must balance the workload among tasks with lower and higher complexity to produce a high quality cartographic product on time and within budget. We argue for increased emphasis on the role of symbol design, which often carries a lower workload than multi-scale mapping based primarily on geometry change. Countering expectations that combining symbol change with geometry change will increase workloads, we argue that in many cases, integration of the two can reduce workloads overall. To demonstrate our points, we describe two case studies drawn from a recent multi-scale mapping and database building project for Ada County, Idaho. We extend the concept of workload balancing, demonstrating that insertion of Level of Detail (LoD) datasets at intermediate scales can further reduce the workload. Previous work proposing LoDs has not reported empirical assessment, and we encourage small and large mapping organizations to contribute to such an effort.”

Spatial Variability of SPT Data using Ordinary and Disjunctive Kriging

Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, Volume 4, Issue 1, 2010, Pages 22 – 31

P. Samui and T. G. Sitharam

“The purpose of this study is to develop a geostastistical model based on ordinary and disjunctive kriging techniques to estimate spatial variability of SPT (N) data in the three-dimensional subsurface of Bangalore. The database consists of 766 boreholes spread over a 220 sq km area, with several N values in each of them. The analysis has been done for corrected SPT (Nc) value. Ordinary kriging produces a linear estimator, whereas disjunctive kriging produces a nonlinear estimator. Knowledge of the semivariogram of the SPT data is used in the kriging theory to estimate the values at points in the subsurface of Bangalore where field measurements are not available. The capability of disjunctive kriging to be a nonlinear estimator and an estimator of conditional probability is explored. A cross-validation (Q1 and Q2) analysis is also done for the developed ordinary and disjunctive kriging models. For the data sets used in this study, disjunctive kriging has shown to be a better estimator than ordinary kriging in terms of reduced kriging variance and the comparison between an estimated and actual value.”

Real-time Monitoring of Water Quality using Temporal Trajectory of Live Fish

Expert Systems with Applications, Volume 37 , Issue 7 (July 2010)

Heng Ma, Tsueng-Fang Tsai, and Chia-Cheng Liu

“This paper proposes a real-time water quality monitoring scheme, which is based on judging time-series motion trajectories of live fish acquired by a CCD camera. The proposed scheme includes a floating-grid method to extract patterns in the motion trajectories and a neural network mechanism to quickly determine the frequency of pattern changes in these trajectories. To validate the proposed methods, several experiments were conducted by changing pH values of the water that houses live fish. The experimental results showed that the proposed methods could effectively differentiate motion trajectories of the fish in an efficient manner. The proposed scheme could be employed as a precautionary warning system for aquatic farms, drinking water treatment plants, and other related industries.”