Structuring Sustainability Science

Sustainability Science, Published Online 23 August 2010

Anne Jerneck, Lennart Olsson, Barry Ness, Stefan Anderberg, Matthias Baier, Eric Clark, Thomas Hickler, Alf Hornborg, Annica Kronsell and Eva Lövbrand, et al.

“It is urgent in science and society to address climate change and other sustainability challenges such as biodiversity loss, deforestation, depletion of marine fish stocks, global ill-health, land degradation, land use change and water scarcity. Sustainability science (SS) is an attempt to bridge the natural and social sciences for seeking creative solutions to these complex challenges. In this article, we propose a research agenda that advances the methodological and theoretical understanding of what SS can be, how it can be pursued and what it can contribute. The key focus is on knowledge structuring. For that purpose, we designed a generic research platform organised as a three-dimensional matrix comprising three components: core themes (scientific understanding, sustainability goals, sustainability pathways); cross-cutting critical and problem-solving approaches; and any combination of the sustainability challenges above. As an example, we insert four sustainability challenges into the matrix (biodiversity loss, climate change, land use changes, water scarcity). Based on the matrix with the four challenges, we discuss three issues for advancing theory and methodology in SS: how new synergies across natural and social sciences can be created; how integrated theories for understanding and responding to complex sustainability issues can be developed; and how theories and concepts in economics, gender studies, geography, political science and sociology can be applied in SS. The generic research platform serves to structure and create new knowledge in SS and is a tool for exploring any set of sustainability challenges. The combined critical and problem-solving approach is essential.”

C. Dana Tomlin to be Inducted into URISA’s GIS Hall of Fame

The Urban and Regional Information Systems Association (URISA) established the GIS Hall of Fame in 2005 to recognize and honor the most esteemed leaders of the geospatial community. To be considered for the GIS Hall of Fame, an individual’s or an organization’s record of contribution to the advancement of the industry demonstrates creative thinking and actions, vision and innovation, inspiring leadership, perseverance, and community mindedness. In addition, nominees must serve as a role model for those who follow. URISA Hall of Fame Laureates are individuals or organizations whose pioneering work has moved the geospatial industry in a better, stronger direction. The first class of inductees included Edgar Horwood, Ian McHarg, Roger Tomlinson, Jack Dangermond, Nancy Tosta, and the Harvard Lab. Gary Hunter was inducted into the Hall of Fame in 2006; Don Cooke and Michael Goodchild, in 2007; and Will Craig, GISP and Carl Reed, PhD joined this esteemed group in 2009. URISA is honored to announce the 2010 GIS Hall of Fame inductee, Dr. C. Dana Tomlin.

Dr. Tomlin will be recognized during the Awards Ceremony on Thursday, September 30, at GIS-Pro 2010: URISA’s 48th Annual Conference in Orlando, Florida.

DR. C. DANA TOMLIN

In the early 1980s, a young Ph.D. student prepared a dissertation on the use of computer algorithms to process raster data in land conservation applications. His groundbreaking research became known as Map Algebra, a vocabulary and conceptual framework for classifying ways to combine map data to produce new maps. While primarily applied to raster data sets (GRID and image data), the same concepts can be applied to many types of cartographic information, and Map Algebra has since been extended to 3D, time and other domains. Map Algebra is used for a broad array of GIS applications including: suitability modeling, surface analysis, density analysis, statistics, hydrology, landscape ecology, real estate, and geographic prioritization. While there are different flavors of Map Algebra, the overall concept is still used today in every GIS application that supports raster calculations.

For his immense contributions to the field of GIS through the development and implementation of Map Algebra, Dr. C. Dana Tomlin has been nominated for induction in the URISA GIS Hall of Fame.

Dr. Tomlin has held various teaching and lecturing positions with Harvard University, Ohio State University, Yale University and the University of Pennsylvania since 1975. His coursework in Landscape Architecture during that timeframe has extensively included GIS and cartographic modeling applications. Dr. Tomlin has also provided private GIS consultation services to public and private organizations since 1975.

In addition to his roles as teacher and lecturer, Dr. Tomlin has been involved in a number of high-level ecological research projects during the same time period. He has served as Principal Investigator or in other leadership capacities on projects for the National Science Foundation, Pew Charitable Trusts, the William Penn Foundation, NASA, IBM, AT&T, and Apple Computers, as well as many others. GIS has figured prominently in all of these projects. Dr. Tomlin has also been widely published in GIS journals and conference proceedings in the United States and abroad.

Dr. Tomlin’s singular contributions to GIS extend across a number of years and a wide variety of applications. As a student at Harvard University in the mid-1970s, Dr. Tomlin developed the Tomlin Subsystem of IMGRID as a master’s thesis. A number of analytical functions in IMGRID were later integrated into Imagine, the world’s leading satellite image processing application developed by ERDAS.

As a doctoral student at Yale University in the late 1970s, and as a junior faculty member at Harvard in the early 80s, Dr. Tomlin developed MAP (the Map Analysis Package), which would come to be recognized as one of the most widely used programs of its kind, with several thousand installations worldwide. Before the term “open source” became widely used, Tomlin donated his source code, documentation and other materials to anyone who asked. While probably not the best financial decision, his generosity and collegiality led to the incorporation of Map Algebra vocabulary, concepts and MAP algorithms being embedded in virtually every raster geographic information systems on the market. His work on this original MAP software has been directly inherited by a long list of subsequent software packages including, OSUMAP, MAP II, MapFactory, MFWorks, MacGIS, IDRISI, MapBox, pMap, MGE, IMGRID and GRASS. A couple of these are described in more detail below.

Throughout the late 1980s, the U.S. Army Corps of Engineers and other federal agencies widely used the open source GRASS application, which derives many of its raster analytical capabilities directly from MAP. Dr. Tomlin served as Principal Investigator for the review portion of this project.

Dr. Tomlin’s landmark book, Geographic Information Systems and Cartographic Modeling, was published in 1990 to expand on his earlier dissertation work. In 1995, AutoDesk acquired a South African company whose raster-analysis product was promoted as the truest implementation of the concepts presented in Tomlin’s book. The result is AutoCAD Map, a geographic information system that couples these capabilities with AutoCAD, the world’s most widely used computer-assisted design (CAD) program.

ESRI’s Spatial Analyst application, as well as its predecessor, the GRID module of ArcInfo, was heavily influenced by Dr. Tomlin’s Map Algebra. ArcGIS is generally recognized as the world’s most widely used GIS software product. Dr. Tomlin has provided private consulting services to ESRI since 1990’s related to the ongoing development of these applications.

Since 1978, Dr. Tomlin’s knowledge and reputation have made him a highly sought-after speaker at colleges, universities, and cartographic organizations across the United States and Canada. He has also been invited to speak at universities as far afield as Madrid, Spain and Sydney Australia. Dr. Tomlin has provided consulting services for a broad array of distinguished organizations such as ERDAS, ESRI, USGS, and the National Ministry of Urban Planning and Ecology in Mexico City, to name just a few. Since 1989, he has also provided editorial and proposal review services to a number of cartographic and environmental journals, including those issued by the Association of American Geographers, American Congress on Surveying and Mapping, Sustainable Forestry, United Nations Environmental Programme, and the United States Department of Agriculture.

With funding provided by a corporate donor in 1990, Dr. Tomlin led an informal group of City and Regional Planning doctoral students at the University of Pennsylvania in founding the Cartographic Modeling Laboratory. The Cartographic Modeling Lab conducts academic research and urban and social policy analysis using GIS and spatial research applications. Dr. Tomlin has been co-director of the lab since 1995.

While Dr. Tomlin’s contributions to the field and the advancement of geographic information science have been substantial, his greatest impact has likely been through the students he has taught and mentored over several decades. He is a masterful teacher and has received teaching awards from both colleagues and students over his career, including the Lindback Award for Distinguished Teaching (2002) and the Perkins Award for Excellence in Teaching (1997).

Early in its development, Dr. Tomlin made the decision to openly share all of the source code, documentation and algorithms associated with Map Algebra with anyone that asked. Consequently, the ideas and source code were incorporated into many commercial GIS software packages, including those by software giant ESRI. Dr. Tomlin’s decision to advance the technology and benefit others without thought to personal remuneration speaks more eloquently of the man’s character than any testimonial ever could. He has continued to freely share his ideas and insight with students, educators, software developers and others in the GIS industry throughout the course of his career.

For his many distinguished and ongoing contributions to the field of GIS, Dr. Tomlin is truly deserving of a place in the URISA GIS Hall of Fame.

For more information about URISA’s GIS Hall of Fame, visit http://www.urisa.org/hall_of_fame or contact URISA at 847/824-6300.

[Source: URISA press release]

Quote of the Day

“From now on, space by itself and time by itself are doomed to fade away into mere shadows, and only a kind of union of the two will preserve an independent reality.”

Hermann Minkowski, 1908

(via Dr. Duane F. Marble)

Esri Releases the Open GeoServices REST Specification

Software Makes Server-side Resources Instantly Available to Thousands of Developers

At the FOSS4G Conference in Barcelona, Spain, today, Esri announced the release of the GeoServices REST Specification. This open specification provides a standard way for Web clients to communicate with geographic information system (GIS) servers through Representational State Transfer (REST) technology. The specification has been opened such that developers can expose the GeoServices API request structure from ArcGIS Server and other non-Esri, back-end GIS servers or processors.

“In many ways, by releasing the GeoServices REST Specification as open technology, Esri is repeating what it did in the early ’90s, releasing shapefiles as an open data format,” explains Dr. Satish Sankaran, Esri product manager for interoperability and standards. “This specification encapsulates a strong platform that allows developers to create services that support rich GIS Web applications and allows geospatial services to be embedded into anything on the Web, including commercial off-the-shelf products, open source, and even mashups. The GeoServices REST Specification helps make all GIS data more open and interoperable on the Web.”

The GeoServices REST Specification for a server implementation is a proven specification that has been widely deployed and exercised in the field and exposes server-side resources to a broad range of clients and applications. The JSON-based, REST-full specification makes the server instantly usable by thousands of developers working in popular client-side development environments with the ArcGIS Web mapping APIs for JavaScript, Flex, Silverlight, iOS, and Android, all of which are powered by the GeoServices REST Specification.

To download the GeoServices REST Specification, visit www.esri.com/opengeoservices.

[Source: Esri press release]

Building Cities for Young People: Why We Should Design Cities with Preteens and Young Teens in Mind

Journal of Urban Design, Volume 15, Issue 3 August 2010 , pages 325 – 334

Lisa M. Weston

“As those with planning authority over large areas consider for whom they are planning, it is recommended that the prototypical citizen be an 11-15 year-old person. Some planners have argued for several decades that young people be taken into consideration when planning urban areas. Recent trends in overweight and obesity in young people have focused attention on children’s ability to safely navigate the path to school. Environmental psychologists have long pointed out the connection between children’s independent travel and self-confidence. However, recent advances in neuropsychiatry indicate the brain is undergoing a second period of growth through adolescence. Furthermore, the areas of the brain related to spatial perception and analysis are growing during this period and subsequent actions hardwire the brain. Therefore, this period of young people’s interaction with the environment is a crucial time and cities should be built with that in mind.”

Hyperspectral Data Classification using Geostatistics and Support Vector Machines

Remote Sensing Letters, Volume 2, Issue 2 2011 , pages 99 – 106

S. Bahria; N. Essoussi; M. Limam

“Hyperspectral imagery combined with spatial features holds promise for improved remote sensing classification. In this letter, we propose a method for classification of hyperspectral data based on the incorporation of spatial arrangement of pixel’s values. We use the semivariogram to measure the spatial correlation which is then combined with spectral features within the stacked kernel support vector machine framework. The proposed method is compared with a classifier based on first-order statistics. The overall classification accuracy is tested for the AVIRIS Indian Pines benchmark dataset. Error matrices are used to estimate individual class accuracy. Statistical significance of the accuracy estimates is assessed based on the kappa coefficient and z-statistics at the 95% confidence level. Empirical results show that the proposed approach gives better performance than the method based on first-order statistics.

Spatial Distribution of Some Soil Properties, Using Geostatistical Methods in Khezrabad Region (Yazd) of Iran

ProEnvironment 3 (2010) 100 – 109

AKBARZADEH A., R. TAGHIZADEH MEHRJARDI

“Soil is an important compartment of the environment that is particularly easy to compromise, sensitive to short and long-term pollution and directly affects sustainability of ecosystems and human health. A prerequisite of ecosystem management decisions is monitoring of the spatial distribution of soil characteristics that geostatistics methods are one of the most advanced techniques. In the present study, kriging, cokriging and IDW methods were used for prediction of spatial distribution of salinity, water at saturation percentage, sodium adsorption ratio and percentage of sand, silt and clay in soils of Khezrabad region in Yazd province of Iran. After data normalization, the variograme was developed. For selecting the best model for competing on experimental variograme, the lower RSS value was used. The best model for interpretative was selected by means of cross validation and error evaluation methods, such as RMSE method. The results showed that kriging and cokriging methods were better than IDW method for prediction of soil properties. Moreover, soil texture and saturation percentage were better predicted by kriging method, where on, soil salinity and sodium adsorption ratio were better determined by cokriging method. The sum of Ca2++Mg2+ and Na+ concentration which were highly correlated with soil salinity and sodium adsorption ratio, respectively, are used as auxiliary parameters in this study. Finally, the soil characteristics maps were prepared, using the best interpolation method in GIS environment.”