From Land Evaluation to Spatio-temporal Decision Support on Afforestation (original) (raw)
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From land evaluation to spatio-temporal decision support on afforestation
2009
Land evaluation delivers a classification and ranking of land units in an intervention zone according to their observed or modelled performance to produce quantity and quality of a given commodity or service . In contemporary land evaluation exercises, the geometric and semantic definition of the land units and their performance attributes are typically stored in a geospatial database. Statistical and geospatial (GIS) tools are then used to assess the land units and to present the results as suitability maps. When current land use and boundary conditions are considered, land evaluation answers the 'What ?' question: 'What is the current performance of a land unit and how does it compare to the other land units in the study area ?'. Rather 'What if ?' questions are at stake when hypothetical or potential use and/or conditions are evaluated.
Upgrading Geographic Information Systems to Spatio-Temporal Decision Support Systems
2011
A GIS is capable of identifying a set of land units meeting positional, temporal, topological and multiple on-site attribute criteria. Based on the combination of its generic analytical functionalities, a GIS can also be used to rank the alternative land units and propose the best or worst ones in terms of the non-weighted or weighted criteria. More advanced multi-criteria decision methods are however not easily incorporated in the GIS-framework so that extension with dedicated tools is required to upgrade the GIS to a full blown sDSS. In this paper we illustrate this upgrading of GIS to sDSS and we argue that when also temporal alternatives are dealt with, the DSS can be termed spatio-temporal. Three statements are made: (i) the presented rationale is challenged by phenomena of spatial and spatio-temporal interaction, (ii) important research avenues are present in order to optimize topological and off-site decision attributes in the spatial and spatio-temporal decision problems and (iii) the forestry domain is very suitable for study and application of all the mentioned issues due to the explicit spatial and temporal nature of the management issues which must be addressed.
Land Use Policy, 2013
In a context where several sectors of society compete for space, land use types must be carefully 14 designed and spatially allocated to guarantee a sufficient level of relevant ecosystem services (ES) in a 15 territory of interest. In this respect, contemporary land use planning involves multiple, often 16 conflicting objectives and criteria. Consequently, major benefits can be expected from spatial decision 17 support systems (sDSS) designed to deal with complex spatial allocation problems. 18
A Spatial Decision Support System for the Management of Public Housing
Recent Advances in Design and Decision Support Systems in Architecture and Urban Planning, 2004
Faculty of the Built Environment UNIVERSITY OF NEW SOUTH WALES i I would like to thank my supervisor Dr Bruno Parolin, and my co-supervisors Dr Bruce Judd and Jim Plume for their experience and the valuable assistance they have provided me throughout this thesis. In particular I would like to acknowledge Margaret Maljkovic from the DoH for making this research possible. Likewise I would like to thank Judy McCormack, Denny Hall, Cameron Matherson, John Gregory and Mark Singer from DoH for their valuable contributions to the research at an early stage and for their continuing support and commitment to the project. The DoH staff, DoH clients, UNSW academics, staff and students form part of the broad network of people who have given valuable input, as are the individual authors and groups referred to in this report, particularly Emilia Conte, Sisi Zlatanova, Bill Randolph and Chris Pettit. I would also like to thank various individuals who have helped the project along the way, including
Spatial Decision Support Systems: Three decades on
Decision Support Systems
A B S T R A C T This paper uses a bibliometric approach to examine the growth of and changes in the Spatial Decision Support Systems (SDSS) field over the past three decades. Bibliographic databases such as Web of Science (WOS) and Scopus provide valuable information on academic disciplines as they contain both the articles published and the articles cited. The articles published, and the disciplinary categorization of where they are published, are indicative of the changing disciplinary balance in SDSS, while the citation links of these papers illustrate the intellectual structure of the field. The analysis shows that despite conceptual links rooted in DSS, the field of SDSS developed largely independently from DSS, with little interaction between both. This is surprising, given the growing importance of spatial applications in DSS and an overlapping interest in business analytics and big data space-time analytics. The paper argues for greater interest in SDSS developments in the DSS field, including emergency response SDSS and public participation SDSS, as two forms of SDSS which extend DSS.
Development of an Agricultural Management Information System based on Open-source Solutions
Procedia Technology, 2014
A common problem associated with ready-made software is that businesses are forced to alter some of their processes in order to fully utilize the product. On the other hand, custom-made applications are specifically designed to accommodate the needs of the client's activities, so the final product will be based on their input during the software development process, and will be fine-tuned to fit the way your business operates. With this approach, an information system designed towards the management of agricultural properties, the Agrifootprint system, was developed. The main goal of the Agrifootprint system was to develop and implement a Web-based Geographic Information System, based on Open-Source technology, with a custom-made, comprehensive and user-friendly geospatial system, in order to integrate and manage data from several small agriculture enterprises. The development process was based in Agile methodologies: SCRUM and Evolutionary Prototyping. This option for agile development process brought the establishment of a relationship of complicity between the customer and the development team due to more real understanding of the requirements raised and the system that will be produced from these.
Multi-Criteria Spatial Decision Support System for Valuation of Open Spaces for Urban Planning
2011
One of the major accompaniments of the globalization is the rapid growing of urban areas. At the end of the 1970th only 38% of world lived in cities, this number increased to more than 50% by 2008. In 2030 two third of all people worldwide are expected to live in cities, many of them in megacities. Urban sprawl is a major environmental concern affecting cities and urban. Urban sprawl depends on the socio-economic situation in the cities. Thus, reducing migration, sustainable handling of the limited resources and “smart growth” are acknowledged as key tasks for urban planning. Coping with these tasks requires precise and adaptive planning instruments. The presented study is part of the research project GAUS (Gaining Additional Urban Space) aiming at inventorying the open space available in urban environments and, moreover, providing flexible multi-criteria spatial decision support system for its development. The method is based on VHR (Very high resolution) optical satellite data (QuickBird (QB) and IKONOS (IK)) which is applied on three study areas: Berlin, Istanbul, and Ruhr Area. Object-based image analysis is applied to map land cover and land use and derive metrics describing urban form and inner-urban structure on multiple scales. The workflow has been standardized and leads to comparable results across different test sites and datasets. In intersection with available GIS (Geographical Information System) and local ancillary data, the outputs of image analysis serve as input for a multi-criteria spatial decision support system. Flexible multi-criteria spatial decision support (MC-SDSS) tool has been created by using MATLAB (Matrix Laboratory) software and its tools (Mapping toolbox etc.). Users can change their weights and parameters with this tool for their different study areas. Urban planners can use final suitability maps of this tool. Thus complex decisions are supported by numerical calculation and spatial visualization in order to come to- - objective solutions. This work contribute to close the gap between remote sensing methods and applied urban planning.
Multi-criteria spatial decision support system for valuation of open spaces for urban planning
Recent Advances in …, 2011
Decision makers historically have indicated that inaccessibility of required geographic data and difficulties in synthesizing various recommendations are primary obstacles to spatial problem solving. Studies have shown that the quality of decisions (i.e., the ability to produce meaningful solutions) can be improved if these obstacles are lessened or removed through an integrated systems approach, such as a spatial decision support system (SDSS). In addition, multicriteria decision making (MCDM) and a wide range of related methodologies offer a variety of techniques and practices to uncover and integrate decision makers' preferences in order to solve "real-world" GIS-based planning and management problems. However, because of conceptual difficulties (i.e., dynamic preference structures and large decision alternative and evaluation criteria sets) involved in formulating and solving spatial decision problems, researchers have developed multicriteria-spatial decision support systems (MC-SDSS). In this paper, we present a general overview of MC-SDSS, briefly review applications of MC-SDSS to a broad range of decision problems, and provide direction for future trends and research in this area.
A SPATIAL DECISION SUPPORT SYSTEM FOR THE MANAGEMENT OF PUBLIC HOUSING
2007
Faculty of the Built Environment UNIVERSITY OF NEW SOUTH WALES i I would like to thank my supervisor Dr Bruno Parolin, and my co-supervisors Dr Bruce Judd and Jim Plume for their experience and the valuable assistance they have provided me throughout this thesis. In particular I would like to acknowledge Margaret Maljkovic from the DoH for making this research possible. Likewise I would like to thank Judy McCormack, Denny Hall, Cameron Matherson, John Gregory and Mark Singer from DoH for their valuable contributions to the research at an early stage and for their continuing support and commitment to the project. The DoH staff, DoH clients, UNSW academics, staff and students form part of the broad network of people who have given valuable input, as are the individual authors and groups referred to in this report, particularly Emilia Conte, Sisi Zlatanova, Bill Randolph and Chris Pettit. I would also like to thank various individuals who have helped the project along the way, including
Journal of Multi-Criteria Decision Analysis, 2011
Decision makers historically have indicated that inaccessibility of required geographic data and difficulties in synthesizing various recommendations are primary obstacles to spatial problem solving. Studies have shown that the quality of decisions (i.e., the ability to produce meaningful solutions) can be improved if these obstacles are lessened or removed through an integrated systems approach, such as a spatial decision support system (SDSS). In addition, multicriteria decision making (MCDM) and a wide range of related methodologies offer a variety of techniques and practices to uncover and integrate decision makers' preferences in order to solve "real-world" GIS-based planning and management problems. However, because of conceptual difficulties (i.e., dynamic preference structures and large decision alternative and evaluation criteria sets) involved in formulating and solving spatial decision problems, researchers have developed multicriteria-spatial decision support systems (MC-SDSS). In this paper, we present a general overview of MC-SDSS, briefly review applications of MC-SDSS to a broad range of decision problems, and provide direction for future trends and research in this area.