Predicting Deer–vehicle Collisions in an Urban Area

Journal of Environmental Management, Available online 22 June 2011

Rob Found and Mark S. Boyce

“Collisions with deer and other large animals are increasing, and the resulting economic costs and risks to public safety have made mitigation measures a priority for both city and wildlife managers. We created landscape models to describe and predict deer–vehicle collision (DVCs) within the City of Edmonton, Alberta. Models based on roadside characteristics revealed that DVCs occurred frequently where roadside vegetation was both denser and more diverse, and that DVCs were more likely to occur when the groomed width of roadside right-of-ways was smaller. No DVCs occurred where the width of the vegetation-free or manicured roadside buffer was greater than 40 m. Landscape-based models showed that DVCs were more likely in more heterogeneous landscapes where road densities were lower and speed limits were higher, and where non-forested vegetation such as farmland was in closer proximity to larger tracts of forest. These models can help wildlife and transportation managers to identify locations of high collision frequency for mitigation. Modifying certain landscape and roadside habitats can be an effective way to reduce deer–vehicle collisions.”

Spatial Data Infrastructure (SDI) Executive Symposium to be Held at the Esri User Conference

Nancy Tosta will moderate the SDI Executive Symposium at the 2011 Esri International User Conference.

Nancy Tosta will moderate the SDI Executive Symposium at the 2011 Esri International User Conference.

Tuesday, 12 July 2011, Room 20D (San Diego Convention Center)

First Session: 8:30 a.m. to 9:45 a.m.; Second Session: 10:15 a.m. to 11:30 a.m.

Moderator:  Nancy Tosta, Principal, Ross & Associates, Ltd.

Senior Executives driving implementations of Spatial Data Infrastructures (SDI) around the world will collaborate in this symposium. These leaders will present their experiences in local, national and trans-national organizations. A panel discussion will follow to explore ideas on how to leverage the value of SDI.

Part 1: Spatial Data Infrastructure (SDI) Executive Symposium – Panel Discussion Senior Executives driving implementations of Spatial Data Infrastructures (SDI) around the world will share ideas and best practices in this symposium. These leaders from local, national and trans-national organizations will present their perspectives on critical directions for SDI. A panel discussion will follow to explore ideas on how to leverage the value of SDI and where it is evolving to in the future.

Part2: Spatial Data Infrastructure (SDI) Executive Symposium – Roundtable Discussion This is the second session of the SDI Senior Executive Symposium. It will expand on topics presented in the previous panel discussion session. Leaders in SDI from local, national and trans-national organizations will collaborate in roundtable discussions on how to effectively address SDI issues.


Mr. Ng Siau Yong, Director, GeoSpatial –Land Asset Management Services, Singapore Land Authority

Ms. Deanna Archuleta , Deputy Assistant Secretary for Water and Science, US Department of Interior

Mr. Mike Wood, Director of (US)

Mr. Adrien Vieira de Mello, Senior IM Specialist, Centre de competence du SITG, Canton on Geneva

Ms. Cathrine Armour, Program Manager, Abu Dhabi Global Environmental Initiative (AGEDI)

Dr. Mukund Rao, Senior Advisor on GIS, Government of India

Mr. Abdul Karim Al Raeisi, Executive Manager-SDC, Abu Dhabi Systems and Information Centre

New Workbook Details Steps in Completing Full-Scale GIS Analysis

Understanding GIS explains the methodology for GIS analysis.

Understanding GIS explains the methodology for GIS analysis.

A new book from Esri Press, Understanding GIS: An ArcGIS Project Workbook, explains the methods, tools, and processes needed to apply full-scale GIS analysis to a spatially based problem. Readers are guided through a hypothetical project of selecting a suitable location for a park near the Los Angeles River in California.

“This book provides the starting data and guidance you need to perform a complete GIS analysis including how to explore the study area, evaluate the data, and build a database; process, edit, and analyze the data; model outcomes; map your findings; and share your results on interactive web maps,” said Christian Harder, who coauthored the book with Tim Ormsby and Thomas Balstrøm.

Understanding GIS: An ArcGIS Project Workbook contains valuable learning materials including

  • Self-paced tutorials
  • ArcGIS Desktop 10 software
  • Access to the online Understanding GIS Resource Center

The premise for the project presented in the book parallels the recent history of the Los Angeles River. In 2005, Los Angeles mayor Antonio Villaraigosa announced the Los Angeles River Revitalization Master Plan, which details the redevelopment of the waterway into wildlife and recreation area pockets from its current state as a flood control channel.

Understanding GIS: An ArcGIS Project Workbook (ISBN: 978-1-58948-242-5, 378 pages, US$79.95) is available at online retailers worldwide, at, or by calling 1-800-447-9778. Outside the United States, visit for complete ordering options or visit to contact your local Esri distributor. Interested retailers can contact Esri Press book distributor Ingram Publisher Services.

[Source: Esri press release]

Validation of a Spatiotemporal Land Use Regression Model Incorporating Fixed Site Monitors

Environmental Science and Technology, 2011, 45 (1), pp 294–299, December 6, 2010

Nectarios Rose, Christine Cowie, Robert Gillett, Guy B. Marks

“Land use regression (LUR) has been widely adopted as a method of describing spatial variation in air pollutants; however, traditional LUR methods are not suitable for characterizing short-term or time-variable exposures. Our aim was to develop and validate a spatiotemporal LUR model for use in epidemiological studies examining health effects attributable to time-variable air pollution exposures. A network of 42 NO2 passive samplers was deployed for 12 two week periods over three years. A mixed effects model was tested using a combination of spatial predictors, and readings from fixed site continuous monitors, in order to predict NO2 values for any two week period over three years in the defined study area. The final model, including terms based on traffic density at 50 and 150 m, population density within 500 m, commercial land use area within 750 m, and NO2 concentrations at a central fixed site monitor, explained over 80% of the overall variation in NO2 concentrations. We suggest that such a model can be used to study the association between variable air pollutant exposures and health effects in epidemiological studies.”

Spatial-temporal Analysis of Non-Hodgkin Lymphoma in the NCI-SEER NHL Case-control Study

Environmental Health 2011, 10:63

David C Wheeler, Anneclaire J De Roos, James R Cerhan, Lindsay M Morton, Richard Severson, Wendy Cozen, and Mary H Ward

“Background: Exploring spatial-temporal patterns of disease incidence through cluster analysis identifies areas of significantly elevated or decreased risk, providing potential clues about disease risk factors. Little is known about the etiology of non-Hodgkin lymphoma (NHL), or the latency period that might be relevant for environmental exposures, and there are no published spatial-temporal cluster studies of NHL.

“Methods: We conducted a population-based case-control study of NHL in four National Cancer Institute (NCI)-Surveillance, Epidemiology, and End Results (SEER) centers: Detroit, Iowa, Los Angeles, and Seattle during 1998-2000. Using 20-year residential histories, we used generalized additive models adjusted for known risk factors to model spatially the probability that an individual had NHL and to identify clusters of elevated or decreased NHL risk. We evaluated models at five different time periods to explore the presence of clusters in a time frame of etiologic relevance.

Crude and adjusted local odds ratios (OR, scale on right) for NHL at a residential lag time of 10 years in the Seattle study area. Crude model: span = 1 (p-value = 0.20); Adjusted model: span = 1 (p-value = 0.15). Model adjusted for age, gender, race, education, and home termite treatment before 1988.

“Results: The best model fit was for residential locations 20 years prior to diagnosis in Detroit, Iowa, and Los Angeles. We found statistically significant areas of elevated risk of NHL in three of the four study areas (Detroit, Iowa, and Los Angeles) at a lag time of 20 years. The two areas of significantly elevated risk in the Los Angeles study area were detected only at a time lag of 20 years. Clusters in Detroit and Iowa were detected at several time points.

“Conclusions: We found significant spatial clusters of NHL after allowing for disease latency and residential mobility. Our results show the importance of evaluating residential histories when studying spatial patterns of cancer.”

Spatial Association of Racial/Ethnic Disparities between Late-stage Diagnosis and Mortality for Female Breast Cancer: Where to Intervene?

International Journal of Health Geographics, 2011, 10:24 (4 April 2011)

Tian N, Wilson JG, and Zhan FB

“Background: Over the past twenty years, racial/ethnic disparities between late-stage diagnoses and mortality outcomes have widened due to disproportionate medical benefits that different racial/ethnic groups have received. Few studies to date have examined the spatial relationships of racial/ethnic disparities between breast cancer late-stage diagnosis and mortality as well as the impact of socioeconomic status (SES) on these two disparities at finer geographic scales.

Geographic distributions of census tracts with significant racial disparities in late-stage diagnosis and mortality for breast cancer using the RD measure for both African-American (a) and Hispanic women (b).

“Methods: Three methods were implemented to assess the spatial relationship between racial/ethnic disparities of breast cancer late-stage diagnosis and morality. First, this study used rate difference measure to test for racial/ethnic disparities in both late-stage diagnosis and mortality of female breast cancer in Texas during 1995-2005. Second, we used linear and logistic regression models to determine if there was a correlation between these two racial/ethnic disparities at the census tract level. Third, a geographically-weighted regression analysis was performed to evaluate if this correlation occurred after weighting for local neighbors.

“Results: The spatial association of racial disparities was found to be significant between late-stage diagnosis and breast cancer mortality with odds ratios of 33.76 (CI: 23.96-47.57) for African Americans and 30.39 (CI: 22.09-41.82) for Hispanics. After adjusting for a SES cofounder, logistic regression models revealed a reduced, although still highly significant, odds ratio of 18.39 (CI: 12.79-26.44) for African-American women and 11.64 (CI: 8.29-16.34) for Hispanic women. Results of the logistic regression analysis indicated that census tracts with low and middle SES were more likely to show significant racial disparities of breast cancer late-stage diagnosis and mortality rates. However, values of local correlation coefficients suggested that the association of these two types of racial/ethnic disparities varied across geographic regions.

“Conclusions: This study may have health-policy implications that can help early detection of breast cancer among disadvantaged minority groups through implementing effective intervention programs in targeted regions.”

Land Use Regression Modeling to Estimate Historic (1962−1991) Concentrations of Black Smoke and Sulfur Dioxide for Great Britain

Environmental Science and Technology, March 29, 2011

John Gulliver, Chloe Morris, Kayoung Lee, Danielle Vienneau, David Briggs, and Anna Hansell

“Land-use regression modeling was used to develop maps of annual average black smoke (BS) and sulfur dioxide (SO2) concentrations in 1962, 1971, 1981, and 1991 for Great Britain on a 1 km grid for use in epidemiological studies. Models were developed in a GIS using data on land cover, the road network, and population, summarized within circular buffers around air pollution monitoring sites, together with altitude and coordinates of monitoring sites to consider global trend surfaces.

“Models were developed against the log-normal (LN) concentration, yielding R2 values of 0.68 (n = 534), 0.68 (n = 767), 0.41 (n = 771), and 0.39 (n = 155) for BS and 0.61 (n = 482), 0.65 (n = 733), 0.38 (n = 756), and 0.24 (n = 153) for SO2 in 1962, 1971, 1981, and 1991, respectively. Model evaluation was undertaken using concentrations at an independent set of monitoring sites. For BS, values of R2 were 0.56 (n = 133), 0.41 (n = 191), 0.38 (n = 193), and 0.34 (n = 37), and for SO2 values of R2 were 0.71 (n = 121), 0.57 (n = 183), 0.26 (n = 189), and 0.31 (n = 38) for 1962, 1971, 1981, and 1991, respectively. Models slightly underpredicted (fractional bias: 0−0.1) monitored concentrations of both pollutants for all years. This is the first study to produce historic concentration maps at a national level going back to the 1960s.”

Spatio-Temporal Analysis Using Urban-Rural Gradient Modelling and Landscape Metrics

Computational Science and Its Applications (ICCSA 2011): Lecture Notes in Computer Science, 2011, Volume 6782/2011, 103-118

Marco Vizzari

“Urbanization can be considered as a particular environmental gradient that produces modifications in the structures and functions of ecological systems. In landscape analysis and planning there is a clear need to develop specific and comparable indicators permitting the spatio-temporal quantification of this gradient and the study of its relationships with the composition and configuration of other land uses. This study, integrating urban gradient modelling and landscape pattern analysis, aims to investigate the spatiotemporal changes induced by urbanization and by other anthropogenic factors.

“Unlike previous studies, based on the transect approach, landscape metrics are calculated diachronically within five contiguous zones defined along the urban to rural gradient and characterized by decreasing intervals of settlement density. The results show that, within the study area, urban sprawl and agricultural land simplification remain the dominant forces responsible for the landscape modifications that have occurred during the period under investigation.”

National Land Survey of Iceland to Deploy ArcGIS for INSPIRE


ArcGIS for INSPIRE helps develop a geospatial platform to support decision-making and engage citizens.

Solution Will Open Access to Data for Improved Governance

The National Land Survey of Iceland (NLSI) is implementing Esri’s ArcGIS for INSPIRE to meet compliance with the European Union’s Infrastructure for Spatial Information in Europe (INSPIRE) Directive. This will ensure that the country’s public data is visible and accessible to institutions, companies, and individuals for improved governance.

By recent law, NLSI is the key player for implementing INSPIRE in Iceland. To help meet this directive, Esri’s distributor in Iceland, Samsyn ehf., is providing the software and services to the national organization. NLSI has been a client of Samsyn since 1994 and in 2009 signed an enterprise license agreement to implement Esri software throughout the organization. Spatial data has been maintained and made available using Esri’s ArcGIS software for many years.

“We believe Esri provides us with the best tools and latest solutions in order to comply with the INSPIRE requirements,” said Eydís Líndal Finnbogadóttir, director of service and SDI at NLSI.

ArcGIS for INSPIRE allows NLSI to fulfill the INSPIRE requirements without having to change its processes for creating and maintaining data. ArcGIS for INSPIRE includes tools to transform data into INSPIRE-compliant databases, which can be published with INSPIRE-compliant web services.

“We are excited to assist in this implementation of INSPIRE in Iceland,” said Stefan Gudlaugsson, GIS manager at Samsyn. “ArcGIS for INSPIRE will make it easier for our clients to create and maintain INSPIRE-compliant data, metadata, and network services, creating a more robust spatial data infrastructure for the country.”

The INSPIRE Directive addresses 34 spatial data themes necessary for environmental applications. These themes are organized into three annexes. ArcGIS for INSPIRE includes data models that comply with INSPIRE Annex I, which is the next milestone required for compliance with the INSPIRE Directive. ArcGIS for INSPIRE will be updated as necessary to include support for Annex II and III data models once they have been finalized.

For more information about ArcGIS for INSPIRE, visit

[Source: Esri press release]

Detection of Arbitrarily-shaped Clusters using a Neighbor-expanding Approach: A Case Study on Murine Typhus in South Texas

International Journal of Health Geographics, 2011, 10:23 (31 March 2011)

Yao Z, Tang J, and Zhan FB

“Background: Kulldorff’s spatial scan statistic has been one of the most widely used statistical methods for automatic detection of clusters in spatial data. One limitation of this method lies in the fact that it has to rely on scan windows with predefined shapes in the search process, and therefore it cannot detect cluster with arbitrary shapes. We employ a new neighbor-expanding approach and introduce two new algorithms to detect cluster with arbitrary shapes in spatial data. These two algorithms are called the maximum-likelihood-first (MLF) algorithm and non-greedy growth (NGG) algorithm. We then compare the performance of these two new algorithms with the spatial scan statistic (SaTScan), Tango’s flexibly shaped spatial scan statistic (FlexScan), and Duczmal’s simulated annealing (SA) method using two datasets. Furthermore, we utilize the methods to examine clusters of murine typhus cases in South Texas from 1996 to 2006.

The most likely cluster detected within the Nueces County

The most likely cluster detected within the Nueces County

“Result: When compared with the SaTScan and FlexScan method, the two new algorithms were more flexible and sensitive in detecting the clusters with arbitrary shapes in the test datasets. Clusters detected by the MLF algorithm are statistically more significant than those detected by the NGG algorithm. However, the NGG algorithm appears to be more stable when there are no extreme cluster patterns in the data. For the murine typhus data in South Texas, a large portion of the detected clusters were located in coastal counties where environmental conditions and socioeconomic status of some population groups were at a disadvantage when compared with those in other counties with no clusters of murine typhus cases.

“Conclusion: The two new algorithms are effective in detecting the location and boundary of spatial clusters with arbitrary shapes. Additional research is needed to better understand the etiology of the concentration of murine typhus cases in some counties in south Texas.”