Jack Dangermond Invites Discussion on Climate Change Adaptation

Spatial RoundtableSpatial Roundtable Conversation Coincides with COP17 in Durban, South Africa

Esri president Jack Dangermond opened a discussion, known as the Spatial Roundtable, about the value of geographic information system (GIS) technology and GeoDesign in responding to the effects of climate change. The conversation runs concurrently with the United Nations Climate Change Conference (COP17) in Durban, South Africa. Esri’s hosted Spatial Roundtable provides an engaging online venue for environmental professionals who wish to weigh in on their visions for geospatial technologies’ role in addressing the challenges of climate change adaptation.

“GeoDesign is a framework for understanding the complex relationships between human-designed settlements and the changing environment,” said Dangermond. “I invite environmental professionals who use GIS in their work to visit the Spatial Roundtable to discuss how organizations and businesses should use this technology to respond to climate change.”

Dangermond began the climate change adaptation conversation by asking, “How can GeoDesign best be applied to climate adaptation in the next 15 years?” A number of thought leaders have already joined the conversation.

  • Dr. Wim Bastiaanssen, scientist and cofounder of eLEAF
  • Dr. Elena Bennett from McGill University
  • Dr. Nguyen Huu Ninh, environmental researcher and recipient of the Nobel Peace Prize
  • Andrea Feunekes, founding partner and co-CEO of Remsoft, Inc.

What do you think? Visit www.spatialroundtable.com and join the conversation. The climate change topic discussion will be active through January 31, 2012, and will remain accessible for review in the Spatial Roundtable archive through 2013.

[Source: Esri press release]

Role of Race and Ethnicity Predictive Modeling and Spatial Analysis in Addressing Health Disparities

Journal of Map & Geography LibrariesJournal of Map & Geography Libraries, Volume 7, Issue 3, 2011

Zachary D. Vernon and Grace H. Ting

“Health disparities are variations in quality or clinical outcomes of care, and they represent a major source of inefficiency within the U.S. health care system. Key strategies in addressing this challenge are developing data on the racial and ethnic demographics of patient populations and determining how best to allocate health plan resources to address disparities.

Overlaying pie charts showing network compositions shows that members in the area of greatest need are served by the PPO network. Source: WellPoint data 10/07–9/08.

Overlaying pie charts showing network compositions shows that members in the area of greatest need are served by the PPO network. Source: WellPoint data 10/07–9/08.

“This article examines the use of mapping tools and spatial analysis in the identification of health disparities among health insurance plan members (health plan members). The article also briefly explores the usage of indirect methodologies to determine a health plan member’s race and ethnicity. In addition, case studies are examined to illustrate the role of mapping and spatial tools in the development of quality improvement interventions.”

Spatially Explicit Inverse Modeling for Urban Planning

Applied Geography

Applied Geography, Volume 34, May 2012

Ricardo Crespo and Adrienne Grêt-Regamey


  • Inverse modeling supports urban decision-making processes based on a desired future.
  • Key variables are identified to compensate negative externalities of densification.
  • Trade-offs between variables lead to more feasible solutions to future problems.
  • Inverse modeling along with Backcasting improve sustainable spatial planning.

“Urban modeling methods have traditionally followed a forward modeling approach. That is, they use data from today’s situation to forecast or simulate future states of an urban system. In this paper, we propose an inverse modeling approach by which we shift our attention from solely forecasting or simulating future states of an urban system to steering it to a desired state in the future via key variables characterizing the system in the present. We first present a theoretical framework for the use of the inverse approach in urban planning. We test the power of the proposed method using a hedonic house price model in a metropolitan area in Switzerland to investigate the negative effects of densification on house prices. The model is calibrated by mixed geographically weighted regression in order to account for spatial variability of both key variables and model outputs. We show how devaluation of house prices caused by densification can be compensated by different levels of socioeconomic, locational as well as structural variables. We illustrate and discuss how trade-offs between variables may lead to more feasible results from an urban planning perspective. We conclude that the proposed method might be valuable for urban planners for developing implementable spatial plans based on future visions. In particular, the fact that other model specifications than hedonic house price model can also be employed to formulate an inverse model application, allows planners to address other type of problems or externalities from urbanization processes such as urban sprawls, environmental pollution or land uses change.”