Spatial ability, urban wayfinding and location-based services:a review and first results

Location-Based Services (LBS) are a new industry at the core of which are GISand spatial databases. With increasing mobility of individuals, the anticipatedavailability of broadband communications for mobile devices and growingvolumes of location specific information available in databases there willinevitably be an increase in demand for services providing location relatedinformation to people on the move. New Information and CommunicationTechnologies (NICTs) are providing enhanced possibilities for navigating ?smartcities?. Urban environments, meanwhile, have increasing spatial complexity.Navigating urban environments is becoming an important issue. The time is ripefor a re-appraisal of urban wayfinding. This paper critically reviews the currentLBS applications and raises a series of questions with regard to LBS for urbanwayfinding. Research is being carried out to measure individuals? spatialability/awareness and their degree of preference for using LBS in wayfinding. Themethodology includes both the use of questionnaires and a virtual reality CAVE.Presented here are the results of the questionnaire survey which indicate therelationships between individuals? spatial ability, use of NICTs and modepreference for receiving wayfinding cues. Also discussed are our future researchdirections on LBS, particular on issues of urban wayfinding using NICTs

Local institutions and Natural Resource Management

As researchers and policy-makers confront the challenges of and opportunities for improving natural resource management, increasing attention is being given to the dynamics of coupled natural-human systems. Interdisciplinary study of these coupled systems has generated considerable research and management innovations. Among these are more intensive research of the emergence and behavior of local institutions and consideration of the potential for voluntary and/or collaborative approaches to supplement conventional natural resource policy and management approaches. Front and center in this line of research are studies of local institutional responses to common pool resource management issues. Over time, this productive line of research is encouraging greater integration of insights across social science fields and identification of systematic patterns in research findings. Responding to such encouragement, this research blends insights from collective action theory, institutional rational choice and the institutional analysis and development (IAD) framework to investigate the distribution and success of resource-based organizations. Moreover, our research makes a unique contribution to this literature by considering the spatial aspects of these institutions' formation, behavior and success. Lake associations are an interesting class of resource-based organizations. These local, lake-centered institutions strive to address management issues using informal and voluntary strategies. Lake associations are most common in lake-rich states, including Minnesota, Michigan, Wisconsin, New York, New Hampshire and Maine. The objectives of these groups vary from narrow (private road maintenance) to broad (watershed health). These organizations allow for lake-centered boundaries including multiple jurisdictions, provide a voice to seasonal property owners, and resolve some issues related to coordination, property rights, and transaction costs. The numerous and diverse lake associations of Maine are the focus of our empirical work. The primary research objective of this analysis is to develop an integrated empirical modeling framework of lake association presence and lake management success. To fulfill this objective, we examined the relative performance of empirical econometric models that ignore and address potential sample selection bias. Because we only observe measures of lake association management success on lakes that have a lake association, the sample is non-random. In our empirical work, entry into the lake association management success sample is further complicated by our reliance on survey data to describe management behavior and performance. A broad secondary research objective is to continue exploring the extent to which the Institutional Development Analysis (IAD) framework can be used to explain the distribution and behavior of Maine lake associations. We assembled an extensive spatial database describing natural and human features of 2,602 Maine lakes (Maine's great ponds; > 10 acres in size) to support this analysis. We integrated this extensive database with a smaller survey-based database describing lake association behavior and natural resource management success. Data describing the distribution and success of lake associations were drawn from non-government organization, federal and state agency databases and primary survey data collected to describe social and economic characteristics of Maine lakes. We captured additional lake and association attributes by manipulating various state and federal GIS databases and creating primary spatial databases. Results to date reveal support for the IAD theoretical framework in describing factors influencing the presence of lake associations. These results offer guidance on how to better integrate the informal approaches of local institutions with more formal, regional government-based management approaches. By understanding where local institutions are likely to form and what issues they are best suited to address, state and federal government agencies can better work with local organizations to address the complexities of natural resource management. Results explaining variation in natural resource management success and the potential gains from an integrated model of presence and success are less robust and are constrained by limited available data describing management behavior and success.local institutions, natural resource management, institutional economics, lake associations, Resource /Energy Economics and Policy,

Load-balanced Range Query Workload Partitioning for Compressed Spatial Hierarchical Bitmap (cSHB) Indexes

abstract: The spatial databases are used to store geometric objects such as points, lines, polygons. Querying such complex spatial objects becomes a challenging task. Index structures are used to improve the lookup performance of the stored objects in the databases, but traditional index structures cannot perform well in case of spatial databases. A significant amount of research is made to ingest, index and query the spatial objects based on different types of spatial queries, such as range, nearest neighbor, and join queries. Compressed Spatial Bitmap Index (cSHB) structure is one such example of indexing and querying approach that supports spatial range query workloads (set of queries). cSHB indexes and many other approaches lack parallel computation. The massive amount of spatial data requires a lot of computation and traditional methods are insufficient to address these issues. Other existing parallel processing approaches lack in load-balancing of parallel tasks which leads to resource overloading bottlenecks. In this thesis, I propose novel spatial partitioning techniques, Max Containment Clustering and Max Containment Clustering with Separation, to create load-balanced partitions of a range query workload. Each partition takes a similar amount of time to process the spatial queries and reduces the response latency by minimizing the disk access cost and optimizing the bitmap operations. The partitions created are processed in parallel using cSHB indexes. The proposed techniques utilize the block-based organization of bitmaps in the cSHB index and improve the performance of the cSHB index for processing a range query workload.Dissertation/ThesisMasters Thesis Computer Science 201

Acnowledging for spatial effects in the Portuguese housing markets

The aim of this paper is to revisit a former paper on the Portuguese housing market (1995), acknowledging for spatial effects in order to interpret housing market changes over 1995-2001. The paper will include a first section devoted to explain the differences between the OLS regression analysis and spatial econometrics, explaining the theoretical background used to develop a spatial lag model with the same database; the second section will show the misspecification problems we found when we ran the same model for after 1995-1998 databases; the third section is devoted to describe new housing literature findings relating housing market evolution with the macroeconomic cycles in Portugal; as a consequence the fourth section will include the method we developed with recent census data, to explain the evolution of the country macroeconomic cycles and the agents’ new behavioural attitudes concerning housing; finally and using spatial analysis we can understand the main changes occurred over the 1995-2001 period. The evaluation of the results contradicts some mainstream scholar and political knowledge to explain spatial inequalities between coast and interior municipalities. Complexity issues seem to be present when we consider the way different market agents make decisions on housing markets, looking this good either as a place to live or an alternative investment asset. In the concluding remarks we raise some new interesting questions for further research.

CAREER/EPSCoR: Geospatial Database-Driven Extraction of Information from Digital Aerial Imagery

This project aims at the advancement of the ability to extract spatial information from digital aerial imagery by taking advantage of geospatial databases to support and guide object extraction operations. It deals with aerial images representing scenes for which prior or complementary information already exists. Examples of such information are pre-existing digital maps, digital terrain models, and spatial information systems in general. The research plan involves: (1) matching images to existing databases for change detection; (2) analysis of scale differences between database information and image; and (3) developing metadata structures to convey accuracy information for objects contained in geospatial databases. By embedding object extraction processes within the framework of spatial information systems, digital image analysis will be able to exploit the advantages offered by the availability of spatial data from various sources and in diverse formats, and, in turn, contribute to the improvement of the temporal and quantitative quality and completeness of the data contained in those sources. The educational aspect of the project includes initiatives designed to take advantage of and incorporate the project research advancements in the graduate and undergraduate curriculum, as well as in the high-school outreach program of the Department of Spatial Information Engineering. A 3-day workshop, with participation of U.S. and international experts is also planned. Combined, the issues addressed in this project will substantially advance science in digital image processing and analysis, and will complement parallel advancements in a variety of related disciplines, most notably digital libraries, geographic information systems, and remote sensor technology

Image mining: trends and developments

[Abstract]: Advances in image acquisition and storage technology have led to tremendous growth in very large and detailed image databases. These images, if analyzed, can reveal useful information to the human users. Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images. Image mining is more than just an extension of data mining to image domain. It is an interdisciplinary endeavor that draws upon expertise in computer vision, image processing, image retrieval, data mining, machine learning, database, and artificial intelligence. In this paper, we will examine the research issues in image mining, current developments in image mining, particularly, image mining frameworks, state-of-the-art techniques and systems. We will also identify some future research directions for image mining

1st INCF Workshop on Sustainability of Neuroscience Databases

The goal of the workshop was to discuss issues related to the sustainability of neuroscience databases, identify problems and propose solutions, and formulate recommendations to the INCF. The report summarizes the discussions of invited participants from the neuroinformatics community as well as from other disciplines where sustainability issues have already been approached. The recommendations for the INCF involve rating, ranking, and supporting database sustainability

Digital Image Access & Retrieval

The 33th Annual Clinic on Library Applications of Data Processing, held at the University of Illinois at Urbana-Champaign in March of 1996, addressed the theme of "Digital Image Access & Retrieval." The papers from this conference cover a wide range of topics concerning digital imaging technology for visual resource collections. Papers covered three general areas: (1) systems, planning, and implementation; (2) automatic and semi-automatic indexing; and (3) preservation with the bulk of the conference focusing on indexing and retrieval.published or submitted for publicatio

Image mining: issues, frameworks and techniques

[Abstract]: Advances in image acquisition and storage technology have led to tremendous growth in significantly large and detailed image databases. These images, if analyzed, can reveal useful information to the human users. Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images. Image mining is more than just an extension of data mining to image domain. It is an interdisciplinary endeavor that draws upon expertise in computer vision, image processing, image retrieval, data mining, machine learning, database, and artificial intelligence. Despite the development of many applications and algorithms in the individual research fields cited above, research in image mining is still in its infancy. In this paper, we will examine the research issues in image mining, current developments in image mining, particularly, image mining frameworks, state-of-the-art techniques and systems. We will also identify some future research directions for image mining at the end of this paper