Qualitative spatial logics for buffered geometries

This paper describes a series of new qualitative spatial logics for checking consistency of sameAs and partOf matches between spatial objects from different geospatial datasets, especially from crowd-sourced datasets. Since geometries in crowd-sourced data are usually not very accurate or precise, we buffer geometries by a margin of error or a level of tolerance a E R≥0, and define spatial relations for buffered geometries. The spatial logics formalize the notions of 'buffered equal' (intuitively corresponding to `possibly sameAs'), 'buffered part of' ('possibly partOf'), 'near' (`possibly connected') and 'far' ('definitely disconnected'). A sound and complete axiomatisation of each logic is provided with respect to models based on metric spaces. For each of the logics, the satisfiability problem is shown to be NP-complete. Finally, we briefly describe how the logics are used in a system for generating and debugging matches between spatial objects, and report positive experimental evaluation results for the system

Matching disparate geospatial datasets and validating matches using spatial logic

In recent years, the emergence and development of crowd-sourced geospatial data has provided challenges and opportunities to national mapping agencies as well as commercial mapping organisations. Crowd-sourced data involves non-specialists in data collection, sharing and maintenance. Compared to authoritative geospatial data, which is collected by surveyors or other geodata professionals, crowd-sourced data is less accurate and less structured, but often provides richer user-based information and reflects real world changes more quickly at a much lower cost. In order to maximize the synergistic use of authoritative and crowd-sourced geospatial data, this research investigates the problem of how to establish and validate correspondences (matches) between spatial features from disparate geospatial datasets. To reason about and validate matches between spatial features, a series of new qualitative spatial logics was developed. Their soundness, completeness, decidability and complexity theorems were proved for models based on a metric space. A software tool `MatchMaps' was developed, which generates matches using location and lexical information, and verifies consistency of matches using reasoning in description logic and qualitative spatial logic. MatchMaps was evaluated by the author and experts from Ordnance Survey, the national mapping agency of Great Britain. In experiments, it achieved high precision and recall, as well as reduced human effort. The methodology developed and implemented in MatchMaps has a wider application than matching authoritative and crowd-sourced data and could be applied wherever it is necessary to match two geospatial datasets of vector data

Matching disparate geospatial datasets and validating matches using spatial logic

In recent years, the emergence and development of crowd-sourced geospatial data has provided challenges and opportunities to national mapping agencies as well as commercial mapping organisations. Crowd-sourced data involves non-specialists in data collection, sharing and maintenance. Compared to authoritative geospatial data, which is collected by surveyors or other geodata professionals, crowd-sourced data is less accurate and less structured, but often provides richer user-based information and reflects real world changes more quickly at a much lower cost. In order to maximize the synergistic use of authoritative and crowd-sourced geospatial data, this research investigates the problem of how to establish and validate correspondences (matches) between spatial features from disparate geospatial datasets. To reason about and validate matches between spatial features, a series of new qualitative spatial logics was developed. Their soundness, completeness, decidability and complexity theorems were proved for models based on a metric space. A software tool `MatchMaps' was developed, which generates matches using location and lexical information, and verifies consistency of matches using reasoning in description logic and qualitative spatial logic. MatchMaps was evaluated by the author and experts from Ordnance Survey, the national mapping agency of Great Britain. In experiments, it achieved high precision and recall, as well as reduced human effort. The methodology developed and implemented in MatchMaps has a wider application than matching authoritative and crowd-sourced data and could be applied wherever it is necessary to match two geospatial datasets of vector data

A method for matching crowd-sourced and authoritative geospatial data

A method for matching crowd-sourced and authoritative geospatial data is presented. A level of tolerance is defined as an input parameter as some difference in the geometry representation of a spatial object is to be expected. The method generates matches between spatial objects using location information and lexical information, such as names and types, and verifies consistency of matches using reasoning in qualitative spatial logic and description logic. We test the method by matching geospatial data from OpenStreetMap and the national mapping agencies of Great Britain and France. We also analyze how the level of tolerance affects the precision and recall of matching results for the same geographic area using 12 different levels of tolerance within a range of 1 to 80 meters. The generated matches show potential in helping enrich and update geospatial data

A method for matching crowd-sourced and authoritative geospatial data

A method for matching crowd-sourced and authoritative geospatial data is presented. A level of tolerance is defined as an input parameter as some difference in the geometry representation of a spatial object is to be expected. The method generates matches between spatial objects using location information and lexical information, such as names and types, and verifies consistency of matches using reasoning in qualitative spatial logic and description logic. We test the method by matching geospatial data from OpenStreetMap and the national mapping agencies of Great Britain and France. We also analyze how the level of tolerance affects the precision and recall of matching results for the same geographic area using 12 different levels of tolerance within a range of 1 to 80 meters. The generated matches show potential in helping enrich and update geospatial data

A logic of directions

We propose a logic of directions for points (LD)over 2D Euclidean space, which formalises primary direction relations east (E), west (W), and indeterminate east/west (Iew), north (N), south (S) and indeterminate north/south (Ins). We provide a sound and complete axiomatisation of it, and prove that its satisfiability problem is NP-complete

Comparative Transcriptome Analysis of Resistant and Susceptible Tomato Lines in Response to Infection by Xanthomonas perforans Race T3

Bacterial spot, incited by several Xanthomonas sp., is a serious disease in tomato (Solanum lycopersicum L.). Although genetics of resistance has been widely investigated, the interactions between the pathogen and tomato plants remain unclear. In this study, tanscriptomes of X. perforans race T3 infected tomato lines were compared to those of controls. An average of 7 million reads were generated with approximately 21,526 genes mapped in each sample post-inoculation at 6h (6 HPI) and 6d (6 DPI) using RNA-sequencing technology. Overall, the numbers of differentially expressed genes (DEGs) were higher in the resistant tomato line PI 114490 than in the susceptible line OH 88119, and the numbers of DEGs were higher at 6 DPI than at 6 HPI. Fewer genes (78 in PI 114490 and 15 in OH 88119) were up-regulated and most DEGs were down-regulated, suggesting that the inducible defense response might not be fully activated at 6 HPI. Accumulation expression levels of 326 co-up regulated genes in both tomato lines at 6 DPI might be involved in basal defense, while the specific and strongly induced genes at 6 DPI might be correlated with the resistance in PI114490. Most DEGs were involved in plant hormone signal transduction, plant-pathogen interaction and phenylalanine metabolism, and the genes significantly up-regulated in PI114490 at 6 DPI were associated with defense response pathways. DEGs containing NBS-LRR domain or defense-related WRKY transcription factors were also identified. The results will provide a valuable resource for understanding the interactions between X. perforans and tomato plants

When worlds collide: combining Ordnance Survey and Open Street Map data

The context of this paper is the progress of national and international spatial data infrastructures such as the UK Location Programme and INSPIRE, contrasted against crowd-sourced geospatial databases such as Open Street Map. While initiatives such as INSPIRE tend towards a top-down process of harmonised data models and services using ISO & OGC standards, the OSM approach is one of tagged data with attribute tags agreed through consensus, but a tag set that can change with time (with inherent related issues of data quality). There is a danger that should the more formal approaches simply ignore the crowd sourced initiatives then they will miss an opportunity to evolve to better meet growing demands for geographic information. In any case both formal and informal data will increasingly coexist begging the question of how an end user gains maximum benefit from both. Ordnance Survey as the national mapping agency of Great Britain provides authoritative datasets with published data specifications driven by a combination of user need and the history of national mapping with a remit to ensure real-world feature changes are reflected in the OS large-scale data within 6 months. OSM in contrast relies on the availability of local mapping enthusiasts to capture changes but through its more informal structure can capture a broader range of features of interest to different sub-communities such as cyclists or horse riders. This research has been carried out to understand the issues of data integration between crowd sourced information and authoritative data. The aim of the research was to look into the mid-term and long-term effects of crowd sourcing technologies for understanding their effects on the change intelligence operations of national mapping agencies (NMAs) in the future. Mobile phones, with more computing power than the desktop machine of 5 years ago and incorporating built-in GPS receivers and cameras have become widespread and give people a multi-sensor capability. This combined with CCTV, sensor webs, RFID etc. offers the potential to make data capture pervasive and ubiquitous. All key sectors of modern economies will be affected by the developments in crowd sourcing of information. The synergies created by new technologies will create the conditions for exciting new developments in geospatial data integration. This has an impact in the spatial data collection domain especially in collecting vernacular and crowd-sourced information. Individual users will be able to use these technologies to collect location data and make it available for multiple applications without needing prior geospatial skills. The basic question behind our research is how do we combine data from authoritative OS data sets with feature-rich, informal OSM data, recognising the variable coverage of OSM while capturing the best of both worlds? There have been previous studies (Al-Bakri and Fairbairn, 2010) focussing on geometric accuracy assessment of crowd-sourced data(OSM) with OS data. Another important context is the rapid developments in Open Source GIS. The availability of free and open source GIS has made possible for large number of government organizations and SMEs to make use of GIS tools in their work. The Open Source Geospatial Foundation (OSGeo) is an excellent example of community initiative to support and promote the collaborative development of open geospatial technologies. OSGeo’s key mission is to promote the use of open source software in the geospatial industry and to encourage the implementation of open standards and standards based interoperability in its projects

Using qualitative spatial logic for validating crowd-sourced geospatial data

We describe a tool, MatchMaps, that generates sameAs and partOf matches between spatial objects (such as shops, shopping centres, etc.) in crowd-sourced and authoritative geospatial datasets. MatchMaps uses reasoning in qualitative spatial logic, description logic and truth maintenance techniques, to produce a consistent set of matches. We report the results of an initial eval- uation of MatchMaps by experts from Ordnance Survey (Great Britain’s National Mapping Authority). In both the case studies considered, MatchMaps was able to correctly match spatial objects (high precision and recall) with minimal human intervention