ESRI / NITLE Partnership Will Expand GIS Use in the Social Sciences

The National Institute for Technology in Liberal Education (NITLE) and ESRI have signed a memorandum of understanding (MOU) that will promote sustainable, cross-curricular uses of geographic information system (GIS) technology throughout NITLE’s network of liberal arts colleges. NITLE, a nonprofit leader in the liberal arts space, helps small colleges and universities develop mission-centric approaches that effectively integrate inquiry, pedagogy, and technology. ESRI is a market leader in GIS that provides complete technical solutions for desktop, mobile, server, and Internet platforms.

ESRI and NITLE are collaborating to determine how liberal arts colleges can better promote spatial thinking and problem solving among students and educators. The development of critical thinking skills is a cornerstone of a liberal arts education.

Sean Connin, program officer for science and technology at NITLE, says, “Because GIS is a cross-disciplinary technology, it is an ideal tool to help students integrate information in a spatial and temporal context and develop unique insights about the world.”

The goals of the MOU include promoting spatial competencies on campus, facilitating cost-effective professional development in GIS, providing campuses with access to GIS software and support, and connecting academic experts to create and promote new GIS curricular materials.

“New models for geospatial support and teaching in the liberal arts are a necessary response to ongoing transformations in spatial technology, communication, and study,” added Connin. By partnering with ESRI, NITLE is seeking to advocate for next-generation strategies that advance spatial teaching and research at small, liberal arts colleges and reduce barriers for these colleges to support these activities.

Observes Toni Fisher, higher education manager at ESRI, “GIS isn’t just about geography; it is a learning platform that promotes analytical thinking in a cross-disciplinary environment. Students in the social sciences, such as history, anthropology, and political science, can all benefit from using GIS. Many of the existing members of NITLE are currently using ESRI software and are looking to take full advantage of its capabilities. This MOU will help spread the understanding and use of this important technology.”

For more information about ESRI’s higher education program visit:

About the National Institute for Technology in Liberal Education

NITLE (pronounced “nightly”) helps liberal arts colleges and universities integrate inquiry, pedagogy, and technology. More than 140 institutions in the NITLE Network use its offerings to enrich undergraduate education and strengthen the liberal arts tradition. Established in 2001 with support from The Andrew W. Mellon Foundation, NITLE is the key organization for small colleges seeking to engage students, use and manage technology strategically to advance their missions, and anticipate the impact of emerging technologies.

About ESRI

Since 1969, ESRI has been giving customers around the world the power to think and plan geographically. The market leader in GIS, ESRI software is used in more than 300,000 organizations worldwide including each of the 200 largest cities in the United States, most national governments, more than two-thirds of Fortune 500 companies, and more than 7,000 colleges and universities. ESRI applications, running on more than one million desktops and thousands of Web and enterprise servers, provide the backbone for the world’s mapping and spatial analysis. ESRI is the only vendor that provides complete technical solutions for desktop, mobile, server, and Internet platforms. (

[Source: NITLE press release]

Efficient Evaluation of Continuous Spatio-temporal Queries on Moving Objects with Uncertain Velocity

GeoInformatica, Volume 14, Number 2 / April, 2010

Yuan-Ko Huang and Chiang Lee

“Continuous Range (CR) query and Continuous K-Nearest Neighbor (CKNN) query are two important types of spatio-temporal queries. Given a time interval [t s , t e ] and a moving query object q, a CR query is to find the moving objects whose Euclidean distances to q are within a user-given distance at each time instant within [t s , t e ]. A CKNN query is to retrieve the K-Nearest Neighbors (KNNs) of this query object q at each time instant within [t s , t e ]. In this paper, we investigate how to process these spatio-temporal queries efficiently under the situation that the velocity of each object is not fixed. This uncertainty on the velocity of object inevitably results in high complexity in processing spatio-temporal queries. We will discuss the complications incurred by this uncertainty and propose two algorithms, namely the Possibility-based possible within objects searching algorithm and the Possibility-based possible KNN searching algorithm, for the CR query and the CKNN query, respectively. A Possibility-based model is designed accordingly to quantify the possibility of each object being the result of a CR query or a CKNN query. Comprehensive experiments are performed to demonstrate the effectiveness and the efficiency of the proposed approaches.”

Multidimensional Map Algebra: Design and Implementation of a Spatio-Temporal GIS Processing Language

Transactions in GIS, Volume 14 Issue 1, Pages 1 – 21, Published Online 17 Jan 2010

Jeremy Mennis

“Due to the increasing volume of spatio-temporal data generated from remote sensing, sensor networks and computational simulation, there is a need for a generic, domain-independent framework for spatio-temporal data analysis. This research presents a generic set of data processing and manipulation tools for spatio-temporal raster data called multidimensional map algebra (MMA). MMA is an extension of conventional map algebra that operates not only on data that are two-dimensional in space but also on data that are: (1) one-dimensional in time; (2) both two-dimensional in space and one-dimensional in time; (3) three-dimensional in space; and (4) both three-dimensional in space and one-dimensional in time. MMA data types, neighborhoods, lags, and functions are presented, including rules for combining data types of different dimensionality within local, focal, and zonal functions. A prototype implementation in JAVA is provided as a demonstration and syntax specification for the functions. Challenges to continued development of MMA include performance and efficiency issues for processing very large multidimensional data sets.”

Fuzzy Spatio-temporal Relations Analysis

7th International Conference on Information Technology: New Generations ITNG 2010, Las Vegas, Nevada

Nadeem SALAMAT and El-hadi ZAHZAH

“There are different families of Spatio-temporal relations such as same-place same-time, same-place different-times, for road networks like overtake, derive beside and many others. These relations describe the relative positions of objects in a spatial scene. In existing techniques, these relations are defined qualitatively. Due to imprecise knowledge information and compensation power to small errors, fuzzy methods are becoming more important.

“In this paper, fuzzy spatio-temporal relations same-place different-time and different-place different time are introduced. To define these relations, histograms of fuzzy Allen relations and fuzzy dissimilarity measure are used.”

Association between Residences in U.S. Northern Latitudes and Rheumatoid Arthritis: A Spatial Analysis of the Nurses’ Health Study

Environmental Health Perspectives, available online 25 March 2010

Verónica M. Vieira, Jaime E. Hart, Thomas F. Webster, Janice Weinberg, Robin Puett, Francine Laden, Karen H. Costenbader, and Elizabeth W. Karlson

“Background: The etiology of rheumatoid arthritis (RA) remains largely unknown although epidemiologic studies suggest genetic and environmental factors may play a role. Geographic variation in incident RA has been observed at the regional level.

“Objective: Spatial analyses are a useful tool for confirming existing exposure hypotheses or generating new ones. To explore further the association between location and RA risk, we analyzed individual level data from U.S. women in the Nurses’ Health Study, a nationwide cohort study.

“Methods: Participants included 461 incident RA cases and 9,220 controls with geocoded addresses followed from 1988-2002. We examined spatial variation using addresses at baseline in 1988 and at time of case diagnosis/censoring of controls. Generalized additive models were used to predict a continuous risk surface, smoothing on longitude and latitude while adjusting for known risk factors. Permutation tests were conducted to test for the overall importance of location and identify areas of statistically significant risk relative to the whole study area.

Results: A statistically significant area of increased RA risk was identified in the northeast U.S. (p-value=0.034). Risk was generally higher at northern latitudes and increased slightly using nurses’ 1988 locations compared to locations at time of diagnosis/censoring. Crude and adjusted models produced similar results.

“Conclusions: Spatial analyses suggest women living in higher latitudes may be at greater risk for RA. Further, RA risk may be greater for locations occurring earlier in residential histories. These results illustrate the usefulness of GAM methods in generating hypotheses for future investigation and supporting existing hypotheses.”

Spatial Analysis Reveals Differences in Soil Microbial Community Interactions between Adjacent Coniferous Forest and Clearcut Ecosystems

Soil Biology and Biochemistry, Article in Press, 2010

Daniel L. Mummey, Jeffrey T. Clarke, Callie A. Cole, Benjamin G. O’Connor, James E. Gannon, and Phillip W. Ramsey

“Knowledge of how forest management influences soil microbial community interactions is necessary for complete understanding of forest ecology. In this study, soil microbial communities, vegetation characteristics and soil physical and chemical properties were examined across a rectangular 4.57 × 36.58 m sample grid spanning adjacent coniferous forest and clearcut areas. Based on analysis of soil extracted phospholipid fatty acids, total microbial biomass, fungi and Gram-negative bacteria were found to be significantly reduced in soil of the clearcut area relative to the forest. Concurrent with changes in microbial communities, soil macroaggregate stability was reduced in the clearcut area, while no significant differences in soil pH and organic matter content were found. Variography indicated that the range at which spatial autocorrelation between samples was evident (patch size) was greater for all microbial groups analyzed in the clearcut area. Overall, less spatial structure could be resolved in the forest. Variance decomposition using principal coordinates of neighbor matrices spatial variables indicated that soil aggregate stability and vegetation characteristics accounted for significant microbial community spatial variation in analyses that included the entire plot. When clearcut and forest areas were analyzed separately, different environmental variables (pH in the forest area and soil organic matter in the clearcut) were found to account for variation in soil microbial communities, but little of this variation could be ascribed to spatial interactions. Most microbial variation explained by different components of microbial communities occurred at spatial scales other than those analyzed. Fungi accounted for over 50% of the variation in bacteria of the forest area but less than 11% in the clearcut. Conversely, AMF accounted for significant variation in clearcut area, but not forest, bacteria. These results indicate broadly disparate controls on soil microbial community composition in the two systems. We present multiple lines of evidence pointing toward shifts in fungi functional groups as a salient mechanism responsible for qualitative, quantitative and spatial distribution differences in soil microbial communities.”

Remote Sensing of the Urban Heat Island Effect across Biomes in the Continental USA

Remote Sensing of Environment, Volume 114, Issue 3, 15 March 2010, Pages 504-513

Imhoff, M.L., Zhang, P., Wolfe, R.E. and Bounoua, L.

“Impervious surface area (ISA) from the Landsat TM-based NLCD 2001 dataset and land surface temperature (LST) from MODIS averaged over three annual cycles (2003–2005) are used in a spatial analysis to assess the urban heat island (UHI) skin temperature amplitude and its relationship to development intensity, size, and ecological setting for 38 of the most populous cities in the continental United States. Development intensity zones based on %ISA are defined for each urban area emanating outward from the urban core to the non-urban rural areas nearby and used to stratify sampling for land surface temperatures and NDVI. Sampling is further constrained by biome and elevation to insure objective intercomparisons between zones and between cities in different biomes permitting the definition of hierarchically ordered zones that are consistent across urban areas in different ecological setting and across scales.

“We find that ecological context significantly influences the amplitude of summer daytime UHI (urban–rural temperature difference) the largest (8 °C average) observed for cities built in biomes dominated by temperate broadleaf and mixed forest. For all cities combined, ISA is the primary driver for increase in temperature explaining 70% of the total variance in LST. On a yearly average, urban areas are substantially warmer than the non-urban fringe by 2.9 °C, except for urban areas in biomes with arid and semiarid climates. The average amplitude of the UHI is remarkably asymmetric with a 4.3 °C temperature difference in summer and only 1.3 °C in winter. In desert environments, the LST’s response to ISA presents an uncharacteristic “U-shaped” horizontal gradient decreasing from the urban core to the outskirts of the city and then increasing again in the suburban to the rural zones. UHI’s calculated for these cities point to a possible heat sink effect. These observational results show that the urban heat island amplitude both increases with city size and is seasonally asymmetric for a large number of cities across most biomes. The implications are that for urban areas developed within forested ecosystems the summertime UHI can be quite high relative to the wintertime UHI suggesting that the residential energy consumption required for summer cooling is likely to increase with urban growth within those biomes.”