Framework for a spatial Decision Support Tool for policy and decision making

The main challenge of developing of a spatial DST (Decision Support Tool) to support the decision making on future livestock production will not be a technical one, but instead a challenge of meeting the con-text requirements of the tool, such as the characteristics of the country-specific spatial plan-ning and decision-making process, the wishes of the potential users of the tool and its output as well as the country-specific policies and regulations. The spatial DST which is being pro-posed in this report therefore does not include complex and state-of-the-art GIS techniques, but instead tries to be as clear and simple as possible, in order to give the potential users a full understanding during the analysis process and with using the output of the tool. A spatial DST can easily become a ‘black box’ if the users do not fully understand the limita-tions of the tool and its output. Despite the fact that output maps of GIS systems may look very detailed and suggest a high degree of accuracy, they are often not. This will entirely de-pend on the availability of reliable and detailed input data. Most likely, many of the produced output maps should be used in an indicative way only. Therefore, the output of the spatial DST needs to be accompanied by supporting information on the reliability of the output and the shortcomings due to unreliable or missing input data, as well as the consequences for use of the output. Therefore, a comprehensive meta-data assessment system is proposed as an in-tegrated part of the spatial DST. The distribution of the output will also require tools to pro-duce more sketch-like presentations, e.g. using fuzzy borders and aggregated maps, which are another important feature of the spatial DST

Decision map for spatial decision making in urban planning

In this paper, we introduce the concept of decision map and illustrate the way this new concept can be used effectively to support participation in spatial decision making and in urban planning. First, we start by introducing our spatial decision process which is composed of five, non-necessary sequential, phases: problem identification and formulation, analysis, negotiation, concertation, and evaluation and choice. Negotiation and concertation are two main phases in spatial decision making but most available frameworks do not provide tools to support them effectively. The solution proposed here is based on the concept of decision map which is defined as an advanced version of conventional geographic maps which is enriched with preferential information and especially designed to clarify decision making. It looks like a set of homogenous spatial units; each one is characterised with a global, often ordinal, evaluation that represents an aggregation of several partial evaluations relative to different criteria. The decision map is also enriched with different spatial data exploration tools. The procedure of the construction of a decision map contains four main steps: definition of the problem (i.e. generation of criteria maps), generation of an intermediate map, inference of preferential parameters, and generation of a final decision map. The concept of decision map as defined here is a generic tool that may be applied in different domains. This paper focuses on the role of the decision map in supporting participation in spatial decision making and urban planning. Indeed, the decision map is an efficient communication tool in the sense that it permits to the different groups implied in the spatial decision process to ‘think visually’ and to communicate better between each other.ou

Orchestrating the spatial planning process: from Business Process Management to 2nd generation Planning Support Systems

Metaplanning can be considered as a necessary step for improving collaboration, transparency and accountability in sustainable and democratic spatial decision-making process. This paper reports current findings on the operational implementation of the metaplanning concept developed by the authors relying on Business Process Management methods and techniques. Two solutions are presented which implement spatial planning process workflows thanks to the development of original spatial data and processing services connectors to a Business Process Management suite. These results can be considered as a first step towards the development of 2nd generation Planning Support Systems

Landslide susceptibility mapping using multi-criteria evaluation techniques in Chittagong Metropolitan Area, Bangladesh

Landslides are a common hazard in the highly urbanized hilly areas in Chittagong Metropolitan Area (CMA), Bangladesh. The main cause of the landslides is torrential rain in short period of time. This area experiences several landslides each year, resulting in casualties, property damage, and economic loss. Therefore, the primary objective of this research is to produce the Landslide Susceptibility Maps for CMA so that appropriate landslide disaster risk reduction strategies can be developed. In this research, three different Geographic Information System-based Multi-Criteria Decision Analysis methods—the Artificial Hierarchy Process (AHP), Weighted Linear Combination (WLC), and Ordered Weighted Average (OWA)—were applied to scientifically assess the landslide susceptible areas in CMA. Nine different thematic layers or landslide causative factors were considered. Then, seven different landslide susceptible scenarios were generated based on the three weighted overlay techniques. Later, the performances of the methods were validated using the area under the relative operating characteristic curves. The accuracies of the landslide susceptibility maps produced by the AHP, WLC_1, WLC_2, WLC_3, OWA_1, OWA_2, and OWA_3 methods were found as 89.80, 83.90, 91.10, 88.50, 90.40, 95.10, and 87.10 %, respectively. The verification results showed satisfactory agreement between the susceptibility maps produced and the existing data on the 20 historical landslide locations

An open and extensible framework for spatially explicit land use change modelling in R: the lulccR package (0.1.0)

Land use change has important consequences for biodiversity and the sustainability of ecosystem services, as well as for global environmental change. Spatially explicit land use change models improve our understanding of the processes driving change and make predictions about the quantity and location of future and past change. Here we present the lulccR package, an object-oriented framework for land use change modelling written in the R programming language. The contribution of the work is to resolve the following limitations associated with the current land use change modelling paradigm: (1) the source code for model implementations is frequently unavailable, severely compromising the reproducibility of scientific results and making it impossible for members of the community to improve or adapt models for their own purposes; (2) ensemble experiments to capture model structural uncertainty are difficult because of fundamental differences between implementations of different models; (3) different aspects of the modelling procedure must be performed in different environments because existing applications usually only perform the spatial allocation of change. The package includes a stochastic ordered allocation procedure as well as an implementation of the widely used CLUE-S algorithm. We demonstrate its functionality by simulating land use change at the Plum Island Ecosystems site, using a dataset included with the package. It is envisaged that lulccR will enable future model development and comparison within an open environment

Vulnerability of the agricultural sector to climate change: The development of a pantropical Climate Risk Vulnerability Assessment to inform sub-national decision making

As climate change continues to exert increasing pressure upon the livelihoods and agricultural sector of many developing and developed nations, a need exists to understand and prioritise at the sub national scale which areas and communities are most vulnerable. The purpose of this study is to develop a robust, rigorous and replicable methodology that is flexible to data limitations and spatially prioritizes the vulnerability of agriculture and rural livelihoods to climate change. We have applied the methodology in Vietnam, Uganda and Nicaragua, three contrasting developing countries that are particularly threatened by climate change. We conceptualize vulnerability to climate change following the widely adopted combination of sensitivity, exposure and adaptive capacity. We used Ecocrop and Maxent ecological models under a high emission climate scenario to assess the sensitivity of the main food security and cash crops to climate change. Using a participatory approach, we identified exposure to natural hazards and the main indicators of adaptive capacity, which were modelled and analysed using geographic information systems. We finally combined the components of vulnerability using equal-weighting to produce a crop specific vulnerability index and a final accumulative score. We have mapped the hotspots of climate change vulnerability and identified the underlying driving indicators. For example, in Vietnam we found the Mekong delta to be one of the vulnerable regions due to a decline in the climatic suitability of rice and maize, combined with high exposure to flooding, sea level rise and drought. However, the region is marked by a relatively high adaptive capacity due to developed infrastructure and comparatively high levels of education. The approach and information derived from the study informs public climate change policies and actions, as vulnerability assessments are the bases of any National Adaptation Plans (NAP), National Determined Contributions (NDC) and for accessing climate finance

TRADEOFFS BETWEEN RURAL DEVELOPMENT POLICIES AND FOREST PROTECTION: SPATIALLY-EXPLICIT MODELING IN THE CENTRAL HIGHLANDS OF VIETNAM

Alleviating rural poverty remains an important objective of development policy in many areas of the world. However, traditional means of increasing rural livelihoods such as increased investments in agricultural intensification measures can have disastrous impacts on natural resources such as forests by greatly increasing incentives for clearing. This paper contains a spatially-explicit model of land use in the Dak Lak province in the Central Highlands of Vietnam. Land use is modeled using a reduced-form multinomial logit model, and policy simulations are conducted. These simulations demonstrate that the adoption of yield-increasing inputs requires concomitant forest protection policies, both in terms of forest area and spatial configuration.International Development,