An interactive learning environment in geographical information systems

The Unigis Learning Station is a computer‐based learning management tool for the Postgraduate Diploma in Geographical Information Systems by distance learning (correspondence). Unigis is an international network of universities co‐operating in the delivery of such courses. The students on Unigis courses are mature mid‐career professionals who study in addition to undertaking full time jobs. The Learning Station offers these students information about the course, resources for independent study, a structured set of exercises, assessments and feedback opportunities, and an integrated and easy way to interact with other course software. Following a brief introduction to the Unigis curriculum, this paper discusses the design of the Learning Station. The roles the Learning Station adopts are outlined, and the range of multimedia and communications tools used discussed. Evaluation of the Learning Station is presented and the issued raised by this provide useful lessons for other computer‐based learning management tools, and the adaptation of the Learning Station to other teaching and learning situations

Web-based Implementation of Winter Maintenance Decision Support System Using GIS and Remote Sensing, May 2005

Winter maintenance, particularly snow removal and the stress of snow removal materials on public structures, is an enormous budgetary burden on municipalities and nongovernmental maintenance organizations in cold climates. Lately, geospatial technologies such as remote sensing, geographic information systems (GIS), and decision support tools are roviding a valuable tool for planning snow removal operations. A few researchers recently used geospatial technologies to develop winter maintenance tools. However, most of these winter maintenance tools, while having the potential to address some of these information needs, are not typically placed in the hands of planners and other interested stakeholders. Most tools are not constructed with a nontechnical user in mind and lack an easyto-use, easily understood interface. A major goal of this project was to implement a web-based Winter Maintenance Decision Support System (WMDSS) that enhances the capacity of stakeholders (city/county planners, resource managers, transportation personnel, citizens, and policy makers) to evaluate different procedures for managing snow removal assets optimally. This was accomplished by integrating geospatial analytical techniques (GIS and remote sensing), the existing snow removal asset management system, and webbased spatial decision support systems. The web-based system was implemented using the ESRI ArcIMS ActiveX Connector and related web technologies, such as Active Server Pages, JavaScript, HTML, and XML. The expert knowledge on snow removal procedures is gathered and integrated into the system in the form of encoded business rules using Visual Rule Studio. The system developed not only manages the resources but also provides expert advice to assist complex decision making, such as routing, optimal resource allocation, and monitoring live weather information. This system was developed in collaboration with Black Hawk County, IA, the city of Columbia, MO, and the Iowa Department of transportation. This product was also demonstrated for these agencies to improve the usability and applicability of the system

Creation of a Spatial Decision Support System as a Risk Assessment Tool Based on Kentucky Tornado Climatology

Tornadoes are one of Mother Nature’s deadliest phenomena. They affect a large region of the United States. The risk of tornadoes is contingent on dynamic atmospheric conditions that are most likely during spring but which can occur anytime of the year, making the storms challenging to forecast. Using geographical information systems (GIS), a web-based spatial decision support system (SDSS) was created to help understand the spatial dimension of tornado risk assessment. The risk values are calculated using Tornado Days rather than taking a crude density measurement. The SDSS hosts GIS web services that are displayed on an Adobe Flex application. The web application allows users to view, research, query and extract information from the attributes of the GIS files. There is also a dynamic risk tool which gives users the ability to click anywhere inside the study area and get the percentage of risk that a tornado will occur within 25 miles of that very point. The web application eliminates users and viewers from conducting their own research and GIS work. In addition, automated updating models and macros were created to update the tornado database on an annual basis

Implementación de sistemas de soporte de decisiones multipropósito a escalas urbana y rural

Tesis presentada para optar al Grado de Doctor en Ciencias NaturalesFil: Entraigas, Ilda. Universidad Nacional de La Plata. Facultad de Ciencias Naturales y Museo; Argentin

Scale, Resolution and Resampling: Representation and Analysis of Remotely Sensed Landscapes Across Scale in Geographic Information Systems.

Earth system scientists are increasingly using the technologies of Geographic Information Systems (GIS) and Remote Sensing (RS) in their analyses of earth system processes and patterns. These investigations take place over a wide range of scales, from the local to the global. Global change researchers focus on both the physical and human dimensions of changes in the earth\u27s landscapes, which occur across a range of scales and may be scale dependent. The way in which landscapes are represented in GIS and RS, using specific spatial data models and data spatial resolutions, affects the subsequent analyses that can be performed. Optimally those analyses are grounded in firm geographical and spatial analytical principles, so as to be appropriate and therefore meaningful interpretations of the data. This research investigates two specific issues of importance to research investigating landscape change across scale, those of resampling and analysis. Four different resampling algorithms, which are used to rescale remotely sensed pixels from higher to lower spatial resolutions, are investigated using Landsat TM data representing the Flint Hills region of Kansas. Two analytical methods for examining scale effects in RS data, local variance analysis and fractal analysis, are used to examine both the effects of the resampling methods on subsequent analyses and the performance of the methods in detecting potential scales of action in the landscapes. Results show differences in the resampling methodologies, which affect the subsequent analyses in different manners. The averaging and convolution methods performed comparably, and are the most reliable type of algorithm examined in this study. Their ongoing use in resampling processes is recommended, recognizing their limitations. The systematic sampling method is not recommended as a resampling procedure. The TM-to-MODIS algorithm, based on the optical properties of the two different resolution sensors, is potentially useful, although the algorithm behaved erratically at times. Both the fractal and local variance methods performed comparably to indicate scale effects in the data, with corresponding results to each other and to the statistical information on the images. As such both methods are deemed appropriate for examining landscapes across scale