Geographically weighted evidence combination approaches for combining discordant and inconsistent volunteered geographical information

There is much interest in being able to combine crowdsourced data. One of the critical issues in information sciences is how to combine data or information that are discordant or inconsistent in some way. Many previous approaches have taken a majority rules approach under the assumption that most people are correct most of the time. This paper analyses crowdsourced land cover data generated by the Geo-Wiki initiative in order to infer the land cover present at locations on a 50 km grid. It compares four evidence combination approaches (Dempster Shafer, Bayes, Fuzzy Sets and Possibility) applied under a geographically weighted kernel with the geographically weighted average approach applied in many current Geo-Wiki analyses. A geographically weighted approach uses a moving kernel under which local analyses are undertaken. The contribution (or salience) of each data point to the analysis is weighted by its distance to the kernel centre, reflecting Tobler’s 1st law of geography. A series of analyses were undertaken using different kernel sizes (or bandwidths). Each of the geographically weighted evidence combination methods generated spatially distributed measures of belief in hypotheses associated with the presence of individual land cover classes at each location on the grid. These were compared with GlobCover, a global land cover product. The results from the geographically weighted average approach in general had higher correspondence with the reference data and this increased with bandwidth. However, for some classes other evidence combination approaches had higher correspondences possibly because of greater ambiguity over class conceptualisations and / or lower densities of crowdsourced data. The outputs also allowed the beliefs in each class to be mapped. The differences in the soft and the crisp maps are clearly associated with the logics of each evidence combination approach and of course the different questions that they ask of the data. The results show that discordant data can be combined (rather than being removed from analysis) and that data integrated in this way can be parameterised by different measures of belief uncertainty. The discussion highlights a number of critical areas for future research

"The Great Blackbury Pie" ~ or ~ Focal Area Bias in Geographically Weighted Analysis

Focal and geographically weighted analyses are commonplace in GIS applications across many fields and disciplines. However, where such analyses are based on ‘dense’ datasets (e.g., a raster surface), they can suffer from an unintended bias towards the periphery of the focal zone (neighbourhood), which (counterintuitively) is exacerbated by the use of distance weighting functions. This paper serves to characterise this problem, which we call focal area bias (FAB), present a proposed correction, and point to extensive simulation-based analysis, which demonstrates both the impact that this effect can have on analyses and the efficacy of our proposed solution

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Understanding MapSwipe: Analysing Data Quality of Crowdsourced Classifications on Human Settlements

Geodata is missing to populate maps for usage of local communities. Efforts for filling gaps (automatically) by deriving data on human settlements using aerial or satellite imagery is of current concern (Esch et al., 2013; Pesaresi et al., 2013; Voigt et al., 2007). Among semi-automated methods and pre-processed data products, crowdsourcing is another tool which can help to collect information on human settlements and complement existing data, yet it’s accuracy is debated (Goodchild and Li, 2012; Haklay, 2010; Senaratne et al., 2016). Here the quality of data produced by volunteers using the MapSwipe app was investigated. Three different intrinsic parameters of crowdsourced data and their impact on data quality were examined: agreement, user characteristics and spatial characteristics. Additionally, a novel mechanism based on machine learning techniques was presented to aggregate data provided from multiple users. The results have shown that a random forest based aggregation of crowdsourced classifications from MapSwipe can produce high quality data in comparison to state-of-the-art products derived from satellite imagery. High agreement serves as an indicator for correct classifications. Intrinsic user characteristics can be utilized to identify consistently incorrect classifications. Classifications that are spatial outliers show a higher error rate. The findings pronounce that the integration of machine learning techniques into existing crowdsourcing workflows can become a key point for the future development of crowdsourcing applications

Using Geographic Relevance (GR) to contextualize structured and unstructured spatial data

Geographic relevance is a concept that has been used to improve spatial information retrieval on mobile devices, but the idea of geographic relevance has several potential applications outside of mobile computing. Geographic relevance is used measure how related two spatial entities are using a set of criteria such as distance between features, the semantic similarity of feature names or clustering pattern of features. This thesis examines the use of geographic relevance to organize and filter web based spatial data such as framework data from open data portals and unstructured volunteer geographic information generated from social media or map-based surveys. There are many new users and producers of geographic information and it is unclear to new users which data sets they should use to solve a given problem. Governments and organizations also have access to a growing volume of volunteer geographic information but current models for matching citizen generated information to locations of concern to support filtering and reporting are inadequate. For both problems, there is an opportunity to develop semi-automated solutions using geographic relevance metrics such as topicality, spatial proximity, cluster and co-location. In this thesis, two geographic relevance models were developed using Python and PostgreSQL to measure relevance and identify relationships between structured framework data and unstructured VGI in order to support data organization, retrieval and filtering. This idea was explored through two related case studies and prototype applications. The first study developed a prototype application to retrieve spatial data from open data portals using four geographic relevance criteria which included topicality, proximity, co-location and cluster co-location. The second study developed a prototype application that matches VGI data to authoritative framework data to dynamically summarize and organize unstructured VGI data. This thesis demonstrates two possible approaches for using GR metrics to evaluate spatial relevance between large data sets and individual features. This thesis evaluates the effectiveness of GR metrics for performing spatial relevance analysis and it demonstrates two potential use cases for GR