Using GIS and K = 3 Central Place Lattices for Efficient Solutions to the Location Set-Covering Problem in a Bounded Plane

Transactions in GIS, Volume 14 Issue 3, June 2010, p 331-349

Stephanie L Straitiff and Robert G Cromley

“One of the simplest location models in terms of its constraint structure in location-allocation modeling is the location set-covering problem (LSCP). Although there have been a variety of geographic applications of the set-covering problem (SCP), the use of the SCP as a facility location model is one of the most common. In the early applications of the LSCP, both potential facility sites as well as demand were represented by points discretely located in geographic space. The advent of geographic information systems (GIS), however, has made possible a greater range of object representations that can reduce representation error. The purpose of this article is to outline a methodology using GIS and K = 3 central place lattices to solve the LSCP when demand is continuously distributed over a bounded area and potential facility sites have not been defined a priori. Although, demand is assumed to exist over an area, it is shown how area coverage can be accomplished by the coverage of a point pattern. Potential facility site distributions based on spacings that are powers of one-third the coverage distance are also shown to provide more efficient coverage than arbitrarily chosen spacings. Using GIS to make interactive adjustments to an incomplete coverage also provides an efficient alternative to smaller spacings between potential facility sites for reducing the number of facilities necessary for complete coverage.”

Modeling of Sensor Data and Context for the Real World Internet

Pervasive Computing and Communications Workshops (PERCOM Workshops), 2010 8th IEEE International Conference

Villalonga, C. Bauer, M. Huang, V. Bernat, J. and Barnaghi, P.

“The Internet is expanding to reach the real world, integrating the physical world into the digital world in what is called the Real World Internet (RWI). Sensor and actuator networks deployed all over the Internet will play the role of collecting sensor data and context information from the physical world and integrating it into the future RWI. In this paper we present the SENSEI architecture approach for the RWI; a layered architecture composed of one or several context frameworks on top of a sensor framework, which allows the collection of sensor data as well as context information from the real world. We focus our discussion on how the modeling of information is done for different levels (sensor and context data), present a multi-layered information model, its representation and the mapping between its layers.”

Spatial Analysis of Air Pollution and Cancer Incidence Rates in Haifa Bay, Israel

Science of the Total Environment, 2010, Jul 12. [Epub ahead of print]

Eitan O, Yuval, Barchana M, Dubnov J, Linn S, Carmel Y, and Broday DM

“The Israel National Cancer Registry reported in 2001 that cancer incidence rates in the Haifa area are roughly 20% above the national average. Since Haifa has been the major industrial center in Israel since 1930, concern has been raised that the elevated cancer rates may be associated with historically high air pollution levels. This work tests whether persistent spatial patterns of metrics of chronic exposure to air pollutants are associated with the observed patterns of cancer incidence rates. Risk metrics of chronic exposure to PM(10), emitted both by industry and traffic, and to SO(2), a marker of industrial emissions, was developed. Ward-based maps of standardized incidence rates of three prevalent cancers: Non-Hodgkin’s lymphoma, lung cancer and bladder cancer were also produced. Global clustering tests were employed to filter out those cancers that show sufficiently random spatial distribution to have a nil probability of being related to the spatial non-random risk maps. A Bayesian method was employed to assess possible associations between the morbidity and risk patterns, accounting for the ward-based socioeconomic status ranking. Lung cancer in males and bladder cancer in both genders showed non-random spatial patterns. No significant associations between the SO(2)-based risk maps and any of the cancers were found. Lung cancer in males was found to be associated with PM(10), with the relative risk associated with an increase of 1mug/m(3) of PM(10) being 12%. Special consideration of wards with expected rates <1 improved the results by decreasing the variance of the spatially correlated residual log-relative risk.”

Geostatistical Analysis of Karst Landscapes

Electronic Journal of Geotechnical Engineering, Vol. 15, 2010

Omar Al-Kouri, Husaini Omar, Mohammed Abu-Shariah, Ahmad Rodzi Mahmu, and Shattri Mansor

“Nowadays, geographical information system (GIS) and remote sensing are emerging as powerful techniques widely applicable in natural resource management and development virtual models. Recent developments in remote sensing, aerial photography and GIS make it possible to detect changes and devise strategies based on these changes. The study focuses on using aerial photography for the detection of changes and effects of mining on geomorphology using the ArcGIS9 extension, Geostatistical Analyst. In addition, the distinctive surface topography of karst landscapes can be characterized in order to compare them with non-karst landscapes, and to determine geological and/or climatic conditions that are responsible for the observed terrain of Kinta Valley Limestone formation at Perak, Malaysia. Geostatistical analyses of the karstic terrain are used in order to distinguish between karst and non-karst area and karst area to observe the variation from the deterministic sample. In contrast, if the range is less, that means the average distance between two points that are similar in height is less and therefore there is more variation in the area. The average range for karst area is 435, while the average range for non-karst area is 690 meters. The difference between the major range and minor range which indicates the degree of anisotropy is more for the karst area and this is an indicator of more variation in spatial structure and autocorrelation of the karst elevation.”