Expert systems (ES) are one of the oldest cognitive technologies utilized for capturing expert knowledge and emulating decision making. They have been used for solving ill-structured or semi-structured problems from various fields including the geospatial domain. After a growth period of spatial ES in the 1990s followed by slower growth in the 2000s, the decline that has occurred during the last seven years is dramatic, raising a twofold question: do they still have a role to play and what is the future of spatial automated reasoning? Both questions need to take into account the current advances in computing technologies such as the Internet of Things (IoT), Big Data, cloud computing, the use of social media, crowdsourcing, the considerable increase in computer power available and the revolution of cognitive computing. The latter is a new era of computing, involving the rise of cognitive platforms such as IBM Watson, which may change the way humans interact with computing systems and make decisions about complex problems. Therefore, ESRI has collaborated with IBM Watson in 2016 to enhance the GIS community and industry by supporting spatial-based decision making. In the light of these considerations, this paper provides a brief overview of ES and spatial ES and tries to answer the aforementioned questions. These facts indicate that the development of integrated spatial based cognitive systems within a cloud-based environment, connected with the IoT and other Big Data sources, which will be embedded within customized problem domain knowledge to automate the reasoning for complex spatial-based problems, is just on the horizon.
INSPIRE is the EU’s authoritative Spatial Data Infrastructure (SDI) in which each Member State provides access to their
spatial data across a wide spectrum of data themes to support policy making. In contrast, Volunteered Geographic
Information (VGI) is one type of user-generated geographic information where volunteers use the web and mobile
devices to create, assemble and disseminate spatial information. There are similarities and differences between SDIs and
VGI initiatives, as well as advantages and disadvantages. Thus, the integration of these two data sources will enhance
what is offered to end users to facilitate decision makers and the wider community regarding solving complex spatial
problems, managing emergency situations and getting useful information for peoples’ daily activities. Although some
efforts towards this direction have been arisen, several key issues need to be considered and resolved. Further to this
integration, the vision is the development of a global integrated GIS platform, which extends the capabilities of a typical
data-hub by embedding on-line spatial and non-spatial applications, to deliver both static and dynamic outputs to support
planning and decision making. In this context, this paper discusses the challenges of integrating INSPIRE with VGI and
outlines a generic framework towards creating a global integrated web-based GIS platform. The tremendous high speed
evolution of the Web and Geospatial technologies suggest that this “super” global Geo-system is not far away.
Volunteered geographic information (VGI) refers to the geographic data compiled and created by individuals which are rendered on the Internet through specific web-based tools for diverse areas of interest. One of the most well-known VGI projects is the OpenStreetMap (OSM) that provides worldwide free geospatial data representing a variety of features. A critical issue for all VGI initiatives is the quality of the information offered. Thus, this report looks into the uncertainty of the OSM dataset for the main road network in Cyprus. The evaluation is based on three basic quality standards, namely positional accuracy, completeness and attribute accuracy. The work has been carried out by employing the Model Builder of ArcGIS which facilitated the comparison between the OSM data and the authoritative data provided by the Public Works Department (PWD). Findings showed that the positional accuracy increases with the hierarchical level of a road, it varies per administrative District and around 70% of the roads have a positional accuracy within 6m compared to the reference dataset. Completeness in terms of road length difference is around 25% for three out of four road categories examined and road name completeness is 100% and around 40% for higher and lower level roads, respectively. Attribute accuracy focusing on road name is very high for all levels of roads. These outputs indicate that OSM data are good enough if they fit for the purpose of use. Furthermore, the study revealed some weaknesses of the methods used for calculating the positional accuracy, suggesting the need for methodological improvements.
Land consolidation is a very effective land management planning approach that aims towards rural/agricultural
sustainable development. Land reallocation which involves land tenure restructuring is the most important, complex and
time consuming component of land consolidation. Land reallocation relies on land valuation since its fundamental
principle provides that after consolidation, each landowner shall be granted a property of an aggregate value that is
approximately the same as the value of the property owned prior to consolidation. Therefore, land value is the crucial
factor for the land reallocation process and hence for the success and acceptance of the final land consolidation plan.
Land valuation is a process of assigning values to all parcels (and its contents) and it is usually carried out by an ad-hoc
committee. However, the process faces some problems such as it is time consuming hence costly, outcomes may present
inconsistency since it is carried out manually and empirically without employing systematic analytical tools and in
particular spatial analysis tools and techniques such as statistical/mathematical. A solution to these problems can be the
employment of mass appraisal land valuation methods using automated valuation models (AVM) based on international
standards. In this context, this paper presents a spatial based linear hedonic price model which has been developed and
tested in a case study land consolidation area in Cyprus. Results showed that the AVM is capable to produce acceptable
in terms of accuracy and reliability land values and to reduce time hence cost required by around 80%.
Land consolidation is considered to be the most effective land management planning approach for controlling land fragmentation and hence improving agricultural efficiency. Land partitioning is a basic process of land consolidation that involves the subdivision of land into smaller sub-spaces subject to a number of constraints. This paper explains the development of a module called LandParcelS (Land Parcelling System) that integrates geographical information systems and a genetic algorithm to automate the land partitioning process by designing and optimising land parcels in terms of their shape, size and value. This new module has been applied to two land blocks that are part of a larger case study area in Cyprus. Partitioning is carried out by guiding a Thiessen polygon process within ArcGIS and it is treated as a multiobjective problem. The results suggest that a step forward has been made in solving this complex spatial problem, although further research is needed to improve the algorithm. The contribution of this research extends land partitioning and space partitioning in general, since these approaches may have relevance to other spatial processes that involve single or multi-objective problems that could be solved in the future by spatial evolutionary algorithms.
Shape analysis is of interest in many fields of spatial science and planning including land management in rural areas. In particular, evaluating the shape of existing land parcels is critical when implementing rural development schemes such as land consolidation. However, existing land parcel shape indices have major deficiencies: completely different shapes of parcels may have the same index value or similar parcel shapes may have different index scores. Thus, there is a clear requirement for a more accurate and reliable measurement method. This paper therefore presents a new parcel shape index (PSI) which integrates a geographical information system (GIS) with a multi-attribute decision-making (MADM) method. It involves the amalgamated outcome of six geometric measures represented by value functions involving a mathematical representation of judgements by experts that compare each geometric measure with that of an optimum parcel shape defined for land consolidation projects. The optimum shape has a PSI value of 1 while the worst shape has a value close to 0. The shape measures used in the model include length of sides, acute angles, reflex angles, boundary points, compactness and regularity. The paper uses data for two case study areas in Cyprus to demonstrate the superiority of the new PSI over three existing shape indices employed in other studies. The methodology utilized here can be implemented in other disciplines dealing with the assessment of objects that can be compared to an optimum.