Understanding spatial data usability

In recent geographical information science literature, a number of researchers have made passing reference to an apparently new characteristic of spatial data known as 'usability'. While this attribute is well-known to professionals engaged in software engineering and computer interface design and testing, extension of the concept to embrace information would seem to be a new development. Furthermore, while notions such as the use and value of spatial information, and the diffusion of spatial information systems, have been the subject of research since the late-1980s, the current references to usability clearly represent something which extends well beyond that initial research. Accordingly, the purposes of this paper are: (1) to understand what is meant by spatial data usability; (2) to identify the elements that might comprise usability; and (3) to consider what the related research questions might be

The arable farmer as the assessor of within-field soil variation

Feasible, fast and reliable methods of mapping within-field variation are required for precision agriculture. Within precision agriculture research much emphasis has been put on technology, whereas the knowledge that farmers have and ways to explore it have received little attention. This research characterizes and examines the spatial knowledge arable farmers have of their fields and explores whether it is a suitable starting point to map the within-field variation of soil properties. A case study was performed in the Hoeksche Waard, the Netherlands, at four arable farms. A combination of semi-structured interviews and fieldwork was used to map spatially explicit knowledge of within-field variation. At each farm, a field was divided into internally homogeneous units as directed by the farmer, the soil of the units was sampled and the data were analysed statistically. The results show that the farmers have considerable spatial knowledge of their fields. Furthermore, they apply this knowledge intuitively during various field management activities such as fertilizer application, soil tillage and herbicide application. The sample data on soil organic matter content, clay content and fertility show that in general the farmers’ knowledge formed a suitable starting point for mapping within-field variation in the soil. Therefore, it should also be considered as an important information source for highly automated precision agriculture systems

Multiphase sampling using expected value of information

This paper explores multiphase or infill sampling to reduce uncertainty after an initial sample has been taken and analysed to produce a map of the probability of some hazard. New observations are iteratively added by maximising the global expected value of information of the points. This is equivalent to minimisation of global misclassification costs. The method accounts for measurement error and different costs of type I and type II errors. Constraints imposed by a mobile sensor web can be accommodated using cost distances rather than Euclidean distances to decide which sensor moves to the next sample location. Calculations become demanding when multiple sensors move simultaneously. In that case, a genetic algorithm can be used to find sets of suitable new measurement locations. The method was implemented using R software for statistical computing and contributed libraries and it is demonstrated using a synthetic data set

Het derde raamwerk

Aan de hand van een oude aula pocket, waarin wetenschappers reflecteerden over hun eigen vakgebied, en daarbij ook voorspellingen deden over hun vakgebied, komt nu Bregt met een beschouwing op zijn vakgebied geo-informatiekunde en geodesie. Hij beziet 'zijn' wereld via drie raamwerke

Developing a goal-oriented SDI assessment approach using GIDEON - the Dutch SDI implementation strategy - as a case study

In 2008, the Dutch government approved the GIDEON document as a policy aiming at the implementation of the National Spatial Data Infrastructure (NSDI) in the Netherlands. The execution of GIDEON should take place by pursuing seven implementation strategies which lead to the achievement of the GIDEON goals. GIDEON also expresses the need to monitor the progress of implementing its strategies and realization of its goals. Currently, the work has been started on monitoring the GIDEON implementation strategies. However, there is still a lack of knowledge and methods to monitor GIDEON goals realization. The challenge is to come up with an approach to assess to what extent these goals are achieved. As a response to the challenge of assessing the GIDEON goals, this paper explores the possibility of using the Multi-view SDI assessment framework (Grus et al., 2007). This paper presents and discusses the method that applies the Multi-view SDI assessment framework, its indicators and measurement methods to create a GIDEON assessment approach. The method of creating a GIDEON assessment approach consists of several procedural steps: formulating specific GIDEON objectives, organizing a one-day workshop involving focus group of specific stakeholders responsible for creation and execution of NSDI, asking the workshop participants to select from a long list those indicators that best measure the achievement of each GIDEON goals. The key step of GIDEON approach is a one-day workshop. The workshop participants represented all organizations that cooperated and/or created GIDEON. The workshop consisted of two parts: first part explained the context of a challenge of assessing GIDEON, second part included participants activity to select and come to the consensus on the list of indicators that would best measure GIDEON goals realization. Additionally, the participants were asked to evaluate and express feedback on the usefulness of the method of creating GIDEON assessment approach. The results show that several indicators that relate to specific SDI goals could be selected by a significant number of workshop participants. The indicators that have been selected are not the final ones yet, but provide a guideline and form a base of what has to be measured when assessing GIDEON goals. Involving the representatives of all parties committed to GIDEON into the process of GIDEON assessment approach creation will strengthen its robustness and acceptance. The results of the feedback form filled by each participant show that the presented method is useful or very useful to create GIDEON assessment approach. Additionally, some of the participants provided already their own indicators which are very specific for Dutch SDI monitoring.The method presented in this research, assuming that SDI goals are defined and the organizations that participate in SDI creation are known, can be applied in any other country to develop country-specific and practical SDI assessment approach

Processing of soil survey data

This thesis focuses on processing soil survey data into user-specific information. Within this process four steps are distinguished: collection, storage, analysis and presentation. A review of each step is given, and detailed research on important aspects of the steps are presented.Observation density, type of soil attributes and selection of observation sites are important aspects in the collection of soil data. The effect of observation density on the accuracy of spatial predictions was investigated in an acid sulphate soil area in Indonesia. It was found that a similar accuracy could be obtained with a marked reduction in observation density.Most attributes collected in soil survey are on an ordinal measurement scale. Commonly used statistics, such as mean, standard deviation and semivariance, and most spatial interpolation techniques are not permissible for this type of data. Ordinal data from a soil survey in Costa Rica are used to illustrate processing possibilities. For instance, the spatial-difference-probability function was proposed for describing the spatial structure of ordinal data.Over the past twenty years the storage of soil survey data in information systems has been receiving much attention. Digital storage is essential for rapid analysis of data. The soil information system of The Netherlands is described.Seven main categories of soil data analysis can be distinguished. Examples of some categories are presented. The differences between interpreted soil maps on scales of 1 : 10 000, 1 : 25 000 and 1 : 50 000 for predicting moisture deficits and changes in crop yield were investigated. No differences in quality were found between the three maps when predicting average values for an area. The best predictions for point locations, however, were obtained with the 1 : 10 000 map.Also a comparison was made between a thematic map produced by spatial prediction from point data (kriging) and one derived from a general-purpose soil map. The thematic map contain attributes that are important for water movement in the soil. No significant difference in purity was found between the two maps. When combining soil data with other spatial data a vector to raster conversion of the soil map is often necessary. Several sheets of the soil map of The Netherlands 1 : 50 000 of different complexity were investigated for the magnitude of the rasterizing error. The regression equations determined related map complexity to rasterizing error. The rasterizing error of a complex map may be as high as 20% for a raster cell size of 4 mm * 4 mm.Two display methods are introduced for the presentation of uncertainty in soil data. The first method yields an isoline map with empirical confidence limits based on the use of kriging and associated estimated kriging variance. The second method yields a map showing the probability that a certain threshold value is exceeded. When presenting soil data in the form of a map, the complexity of the map pattern has an important influence on its readability. Six complexity measures for maps were compared. The fragmentation index was selected as the best measure for evaluating map complexity