The size, volume, variety, and velocity of geospatial data collected by geo-sensors, people, and organizations are increasing rapidly. Spatial Data Infrastructures (SDIs) are ongoing to facilitate the sharing of stored data in a distributed and homogeneous environment. Extracting high-level information and knowledge from such datasets to support decision making undoubtedly requires a relatively sophisticated methodology to achieve the desired results. A variety of spatial data mining techniques have been developed to extract knowledge from spatial data, which work well on centralized systems. However, applying them to distributed data in SDI to extract knowledge has remained a challenge. This paper proposes a creative solution, based on distributed computing and geospatial web service technologies for knowledge extraction in an SDI environment. The proposed approach is called Knowledge Discovery Web Service (KDWS), which can be used as a layer on top of SDIs to provide spatial data users and decision makers with the possibility of extracting knowledge from massive heterogeneous spatial data in SDIs. By proposing and testing a system architecture for KDWS, this study contributes to perform spatial data mining techniques as a service-oriented framework on top of SDIs for knowledge discovery. We implemented and tested spatial clustering, classification, and association rule mining in an interoperable environment. In addition to interface implementation, a prototype web-based system was designed for extracting knowledge from real geodemographic data in the city of Tehran. The proposed solution allows a dynamic, easier, and much faster procedure to extract knowledge from spatial data.
Finding an appropriate vacant lot to buy is one of the most important land marketing problems in Tehran, Iran. Typically, the land selection and purchase processes in Tehran city are based on face-toface meetings. Despite the face-to-face land sale and purchase strategies, a few online geo-marketing or real-estate websites have been developed. However, these geo-marketing real-estate web services have two main limitations. The services have focused on simple searches and selections from a set of predefined land parcels according to some basic criteria, such as land price, area etc. They do not consider spatially explicit criteria and spatial analyses relevant to a land selection process (e.g. proximity to transportation stations). The services are lacking a proper scientific foundation for spatial multicriteria selection of land parcels. Moreover, the alternative land parcels are dynamically created by new sellers. However, the services are hard-coded based on a set of predefined land parcels and fail to support evaluation of the parcels added by new owners. To overcome these limitations, the study proposes a geo-marketing decision support system by integrating geographic information system (GIS) and multi-criteria decision analysis (MCDA) techniques into the web platform. The GIS-based MCDA tools provide appropriate analytical approaches and platforms to support decision makers/people in spatial decision-making processes based on individual values and interests. The paper demonstrates the implementation of a geo-marketing decision support system for land selection and purchase processes in district no. 22 of Tehran. ARTICLE HISTORY
ABSTRACT:Urban cellular automata is used vastly in simulating of urban evolutions and dynamics. Finding an appropriate neighbourhood size in urban cellular automata modelling is important because the outputs are strongly influenced by input parameters. This paper investigates the impact of spatial filters on behaviour and outcome of urban cellular automata models. In this study different spatial filters in various sizes including 3*3, 5*5, 7*7, 9*9, 11*11, 13*13, 15*15 and 17*17 cells are used in a scenario of land-use changes. The proposed method is examined changes in size and shape of spatial filter whereas the resolution was kept fixed. The implementation results in Ahvaz city demonstrated that KAPPA index is changed in different shapes and types at the time when different spatial filters are used. However, circular shape with size of 5*5 offers better accuracy.
Waste disposal site selection is complex and multifaceted decision making process. In this regard to find appropriate sites, there are different local, national and international standards. These standards emphasize on certain criteria and related on different environmental, economic, social and political considerations. Lack of a comprehensive, integrated and efficient waste disposal site selection approach for determination of the proper sites stems from different experts comments. Since every expert in such problems doesn't have adequate knowledge of all aspects about the subject; their opinions are facing with uncertainty. This study aims to modeling uncertainty of waste disposal site selection based on Dempster-Shafer Theory (DST). The main contribution of this study is the reduction of uncertainty in the standard combination according to experts using DST. In this research three main standards including 'Environment Protection Agency of Iran (EPAI)', 'Management and Planning Organization of Iran (MPOI)' and 'Minnesota Pollution Control Agency (MPCA)' standards are considered and then site selection performed for each standard independently. In order to combining these standards, opinions of 40 experts are attained through DST as an integration approach that is able to manage the uncertainty. These comments are examined by DST and gained results are depicted as waste disposal location. Finally, the achieved results are compared with the results of simple weighting method (average of the opinion experts). Results showed that standard weight in questioning, that does not follow the normal, gives a lot of uncertainty in analysis that DST significantly reduces its impact.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.