Bridging the Past and Present: A GIS-Based System for Managing Ankara’s Multi-Layered Urban Heritage

A Multiobjective SDSS for Management of Urbanizing Watersheds: The Case of the Lower Kaskaskia Basin, Illinois

Having analyzed the current status and existing problems of Geographic Information Systems (GIS) applications in epidemiology, this chapter proposes a method to establish a spatial decision support system (SDSS) for the prevention of epidemic diseases by integrating the COM GIS, spatial database, gps, remote sensing, and communication technologies, as well as ASP and ActiveX software development technologies. One important issue in constructing the SDSS for epidemic disease prevention concerns the incorporation of epidemic spread models in a GIS. The chapter begins with a description of the capabilities of GIS in epidemic prevention. Some established models of an epidemic spread are studied to extract essential computational parameters. A technical schema is then proposed to integrate epidemic models using a GIS and relevant geospatial technologies. The GIS and modeling platforms share a common spatial database and the modeled results can be visualized spatially by desktop and Web clients. A complete solution for establishing the SDSS for epidemic disease prevention based on the model integrating methods and the ArcGIS software is suggested in this chapter. The proposed SDSS comprises several sub-systems: data acquisition, network communication, model integration, epidemic disease information spatial database, epidemic disease information query and statistical analysis, epidemic disease dynamic surveillance, epidemic disease information spatial analysis and decision support, as well as epidemic disease information publishing based on the Web GIS technology. The design process and sample VC and VB programming codes of the epidemic case precaution are used as an example to illustrate the basic principles and methods of the system development that integrates GIS functions with models of epidemic spread. A case study of AIDS in the Yunnan Province of China exemplifies the systems spatial analytical functions through its spatial database access and statistical analysis tools.

Geographic Information Systems (GIS) have been widely used for spatial data manipulation for hydrologic model operations and as a supporting tool to develop spatial decision support systems (SDSS). Information technologies, including GIS and the Internet, have provided opportunities to overcome many of the limitations of computer-based models in terms of data preparation and visualisation, and provide the possibility to create integrated SDSS. This paper examines the relationship between changes in GIS technology and watershed management SDSS. It also describes a conceptual web-based SDSS framework in terms of system components and data flow. A prototype watershed management web-based SDSS that utilises the conceptual framework is examined (URL: http://pasture.ecn.purdue.edu/~watergen/owls). The SDSS uses web-GIS for watershed delineation, map interfaces and data preparation routines, a hydrologic model for hydrologic/water quality impact analysis and web communication programs for Internet-based system operation. The web-based SDSS can be helpful for watershed management decision-makers and interested stakeholders. The watershed management SDSS also provides insight into the role of GIS and information technologies in creating readily accessible and useable SDSS capabilities.

Due to their extension and complexity, problems concerned with geographic space are usually classified as ill or unstructured. For these reasons decision-making processes (DMP) that conclude with the selection of optimal or satisfactory solutions require effective and efficient means of support. Spatial Decision Support Systems (SDSS) are computer systems developed to support DMP in which the problems have geographic dimensions and whose structure is complex or impossible to delineate. These systems are functionally composed of data and scientific models managed with the aim of providing maximised support to DMP. The component represented by spatial data is one of the main obstacles that have to be overcome for SDSS to give effective support. Geographic Information Systems (GIS) have been a paradigm in SDSS development strategies, fundamentally due to their capacity to collect, store, and handle spatial data. The scientific modelling component, represented by mathematical models of natural physical processes, usually is implemented in SDSS through specific software subsystems. Especially in the last seven years there has been great scientific interest in SDSS accompanied by a proliferation of adjacent technologies. Some authors have attempted to classify SDSS without, however, reaching a generally accepted proposal. This lack of clarity has made detailed analysis of existing systems and development of new projects more difficult. The aims of this research were: a) identify and analyse the main variables that determine current application software development methodologies in the field of SDSS; b) provide a taxonomy of these methodologies, with the objectives of being generic, general, and practical; c) identify and analyse the strategy that presents the greatest flexibility to develop effective SDSS. The research was based on a bibliographic survey and is part of the doctoral thesis of first author. The paper presents and criticises relevant issues about different development strategies and the respective systems produced. Three main variables were identified and guided the development of the taxonomy of the strategies. The paper proposes five taxonomic classes of coupling GIS technology and scientific modelling subsystems. These classes are defined and the paper argues that they are sufficient to categorise the main current methodologies as well as suggest places to expect new technologies. Each class is also exemplified with a number of SDSS applied to the watershed DMP domain. Within these classes, the research identified and analysed the one most likely to show the greatest flexibility to develop effective SDSS

Mobility management in telecommunication networks is basically concerned with managing mobility of a terminal covering location management and handoff management. Handoff management allows a call in progress to continue as the mobile terminal (MT) changes channels or moves between cells. Location management deals with locating roaming mobile terminals (MT) for call delivery. Future networks will have to accommodate both user and network mobility and mobility management will become increasingly important. In present day networks the MT periodically performs location registration to notify the network of its new access point and store changes to its user location profile. The network pages possible user locations based on a mobility model or a location profile available to achieve call delivery with minimal signaling overheads. In this paper we propose a new location tracking strategy based on spatial reasoning decision support systems. Possible user locations are computed by querying a Geographic Information System (GIS) which has a decision support system to enable such queries. We use a contextual fuzzy cognitive map (FCM) as a spatial decision support system along with a GIS. The proposed method is evaluated for paging delays and database query overheads in simulated conditions.

A prototype spatial decision support system (SDSS) has been designed for contingency planning for emergency evacuations which combines simulation techniques with spatial data handling and display capabilities of a geographical information system (GIS). It links together the topographical support and analysis provided by the GIS–ARC/INFO, with a simulation model designed to simulate the dynamics of an evacuation process in detail. Our aim has been to design a SDSS so that it provides an interactive evacuation simulator with dynamic graphics that allows for experimentation with policies by providing rapid feedback from the simulation. The idea is that emergency planners will be able to use the SDSS to experiment with emergency evacuation plans in order to plan for different contingencies. This paper concentrates on the issues involved in designing an effective integration link interface between the GIS and the simulation model when building a SDSS of this type.

BackgroundFollowing renewed international attention and political commitment, malaria elimination is back on the world health agenda. Whilst there is currently a global focus and dedication of resources towards elimination, malaria programs pursuing this goal face significant challenges in meeting increased operational requirements, particularly in resource-poor and remote settings. Key priorities of elimination include the need to ensure the effective delivery of scaled-up services and interventions at optimal levels of coverage in target areas; the ability to rapidly identify transmission foci and target appropriate responses; and the capacity to readily provide detailed and accurate data to generate useful information, knowledge and evidence throughout all phases of program implementation. A need for further research into new tools and approaches to support intensified malaria control and elimination is identified within the Roll Back Malaria (RBM) Global Malaria Action Plan (GMAP) as a core global strategy.AimsAims of the thesis were to develop and implement a spatial decision support system (SDSS) for malaria elimination to guide program priorities in Solomon Islands and Vanuatu including: modern geographical reconnaissance (GR) mapping and data collection; frontline vector-control and malaria prevention intervention management; and high resolution geospatial surveillance-response.MethodsCustomized geographic information system (GIS) based SDSS were developed at a provincial level to support progressive malaria elimination campaigns in Solomon Islands and Vanuatu. Geographical Reconnaissance (GR) surveys were conducted in the elimination provinces of Temotu, Solomon Islands and Tafea, Vanuatu in 2008 and 2009 to rapidly map and enumerate households and collect associated population and household structure data using integrated handheld computers and global positioning systems (GPS). A SDSS approach was adopted to guide the planning, implementation and assessment of frontline focal indoor residual spraying (IRS) interventions on Tanna Island, Vanuatu in 2009. High-resolution surveillance-response systems were also developed in the elimination provinces of Temotu and Isabel, Solomon Islands and Tafea, Vanuatu in 2011 to support rapid reporting and mapping of confirmed cases by household, automatic classification and mapping of active transmission foci, and the generation of areas of interest (AOI) regions to conduct targeted response. Quantitative and qualitative analysis were conducted throughout the course of the thesis to assess the performance and acceptability of the SDSS-framework. A retrospective overall examination of the customised SDSS applications developed to support elimination in Solomon Islands and Vanuatu was also conducted in 2013 to review the role of SDSS for malaria elimination.ResultsA total of 10,459 households were mapped and enumerated, with a population of 43,497 and 30,663 household structures recorded and uploaded into the SDSS framework during three GR surveys. Household maps, as well as detailed summaries were extracted from the SDSS and used to describe the spatial distribution of the target population in the elimination provinces. A map-based SDSS application was used identify the focal IRS boundary on Tanna Island and delineate 21 individual operation areas comprising 187 settlements and 3,422 households. Household distribution maps, data summaries and checklists were generated to support IRS implementation. Spray coverages of 94.4% of households and 95.7% of the population were achieved. Spray status maps were also produced at a sub-village level to visualise the delivery and coverage of IRS by household. A total of 183 confirmed cases were reported and mapped in the SDSS and used to classify active transmission foci within a target population of 90,354. Automated AOI regions were also generated to identify response areas. Of the reported confirmed cases, 82.5% were successfully mapped at the household level, with 100% of remaining cases geo-referenced at a village level. By 2013, a total of 20,733 households, 55,711 structures and a population of 91,319 were recorded and mapped in the SDSS in four elimination provinces in Solomon Islands and Vanuatu. The framework has been used to guide both IRS and long-lasting insecticidal net (LLIN) distribution achieving an overall household coverage of 97.5% and 91.7% respectively. High-resolution surveillance-response applications are also ongoing. A high acceptability of the SDSS was recorded from stakeholder surveys and group discussions.ConclusionsThis thesis presents an SDSS-based approach to addressing scaled-up demands of elimination utilising modern geospatial tools and technology in remote and challenging settings. Geospatial systems developed to support Pacific Island progressive malaria elimination campaigns demonstrate the suitability of a SDSS-framework as a platform to rapidly collect, store and extract essential data throughout key phases of program implementation; effectively manage and ensure essential services are delivered at optimal levels of coverage in target areas; and actively locate and classify transmission to guide swift and appropriate responses. Findings presented in the thesis also highlight the importance of the integral role of malaria program personnel, and the need to transition from traditional styles of monitoring and evaluation to active surveillance-response using minimal essential data integrated using modern SDSS technology.

This chapter discusses the use of geographic information systems (GIS) for spatial decision support systems (SDSS). It argues that the increased availability in spatial business data has created new opportunities for the use of GIS in creating decision tools for use in a variety of decisions that involve spatial dimensions. This chapter identifies visualization and analytical capabilities of GIS that make such systems uniquely appropriate as decision aids, and presents a conceptual model for measuring the efficacy of GIS-based SDSS. The discussions on the applications of SDSS and future enhancements using intelligent agents are intended to inform practitioners and researchers of the opportunities for the enhancement and use of such systems.

Geographic Information Systems (GIS) can enhance the efficiency and effectiveness of decision makingin the residential real estate industry. They can organize, manage, and analyze information in waysthat were not possible with traditional information management systems. Although GIS are now usedto perform specific business functions, their use can be magnified and extended through the creationof enterprise-wide spatial decision support systems (SDSS).This article provides a conceptual framework for the development of enterprise-wide SDSS. The firstpart of the article discusses the nature of real estate decision making and investment analysis, payingspecial attention to residential real estate. It also reviews different approaches to SDSS development.The second part of the article discusses enterprise-wide information architecture planning and specifiesa conceptual framework for SDSS development. It then discusses issues related to technologytransfer and SDSS implementation.

woodss — a spatial decision support system based on workflows

Geographical information systems (GIS) become a popular tool in applications that include spatial objects and relations in the real world. The fast-technological expansions in the applications of web-based, simplifying the interaction between GIS and users. A fundamental problem remains available when dealing with a large amount of data in the GIS. Though the GIS strengths in the process of the spatial domain analysis, the systems of GIS are almost uninformed concerning the handling and analysis in the temporal domain. GIS systems find the path between two points or locations depending on time and speed. Therefore, in these days, considerable efforts are guided on this field towards the short time representation on GIS and safe this road. For accomplishing the particular goals of this paper and improve the usefulness of Google maps for the applications of GIS, this work builds a methodological structure to process vector and attribute data and visualise it. The conceptual model utilised here includes three main portions: firstly, the database tier; secondly, the application tier; and thirdly the client tier. This paper proposes a new spatial decision support system in GIS to find the best route between two locations based on different factors; it calculates the shortest and the fastest route depending on either length like other decision support systems or on other features like road safety or quality road by assigning a value to each road using intelligent algorithms (Dijkstra, Astar). The obtained results show decreasing in the time required to find the best result and adding more features as a cost to help the user to select the suitable road.

BackgroundA high-resolution surveillance-response system has been developed within a geographic information system (GIS) to support malaria elimination in the Pacific. This paper examines the application of a GIS-based spatial decision support system (SDSS) to automatically locate and map the distribution of confirmed malaria cases, rapidly classify active transmission foci, and guide targeted responses in elimination zones.MethodsCustomized SDSS-based surveillance-response systems were developed in the three elimination provinces of Isabel and Temotu, Solomon Islands and Tafea, Vanuatu. Confirmed malaria cases were reported to provincial malaria offices upon diagnosis and updated into the respective SDSS as part of routine operations throughout 2011. Cases were automatically mapped by household within the SDSS using existing geographical reconnaissance (GR) data. GIS queries were integrated into the SDSS-framework to automatically classify and map transmission foci based on the spatiotemporal distribution of cases, highlight current areas of interest (AOI) regions to conduct foci-specific targeted response, and extract supporting household and population data. GIS simulations were run to detect AOIs triggered throughout 2011 in each elimination province and conduct a sensitivity analysis to calculate the proportion of positive cases, households and population highlighted in AOI regions of a varying geographic radius.ResultsA total of 183 confirmed cases were reported and mapped using the SDSS throughout 2011 and used to describe transmission within a target population of 90,354. Automatic AOI regions were also generated within each provincial SDSS identifying geographic areas to conduct response. 82.5% of confirmed cases were automatically geo-referenced and mapped at the household level, with 100% of remaining cases geo-referenced at a village level. Data from the AOI analysis indicated different stages of progress in each province, highlighting operational implications with regards to strategies for implementing surveillance-response in consideration of the spatiotemporal nature of cases as well as logistical and financial constraints of the respective programmes.ConclusionsGeospatial systems developed to guide Pacific Island malaria elimination demonstrate the application of a high resolution SDSS-based approach to support key elements of surveillance-response including understanding epidemiological variation within target areas, implementing appropriate foci-specific targeted response, and consideration of logistical constraints and costs.

Geographic Informat ion Systems (GIS) have become an effective tool for decision support. Spatial Decision Support System (SDSS) is a relatively new field developed based on Geographic In formation System (GIS) and Decision Support System (DSS). SDSS will be an important component of DSS applications in future. This trend will be driven by the relevance of spatial info rmation as a co mponent of the info rmation needed for a wide range of decisions. This class of DSS will make an important contribution, not because of its use of the latest technology, but because it will allo w decision makers incorporate a spatial dimension in their decision making. So Spatial Decision Support Systems (SDSS) are decision support systems where spatial properties of the data to be analyzed play a major ro le in decision making special in many sectors. Maps and geographic features can be used to show decision related informat ion and relationship between objects to solve important problems like in spreading diseases and industrial pollution.

Recently, various sorts of infections are spreading among humans and then, it was changed into a disease. It will be easier to prevent and get treatment for the infected disease if it is diagnosed initially like how much percentage is affected, and this could be done by employing the Spatial Decision Support System (SDSS). SDSS, which had been prolonged to offer knowledge workers with Decision-Making (DM) tools and support the data, is typically a Geographic Information System (GIS). DSS concept is grounded on Dialog, Data, and Model (DDM), and among these ‘3’ capabilities, a well-design SDSS should have balance. The development of specific SDSS is facilitated by the DSS tools that could further be deployed for developing a variety of specific SDSS. Thus, human infection diseases, SDSS, SDSS for preventing human infection using ArcGIS, and the application of SDSS in other fields using Aeronautical Reconnaissance (ArcGIS) with its different models had been explained in this paper. DOI : https://doi.org/10.52783/pst.897

Potential of Geographic Information Systems for Refugee Crisis: Syrian Refugee Relocation in Urban Habitats