National Center for Geocomputation
National Center for Geocomputation
Harris P.,Rothamsted Research |
Brunsdon C.,National Center for Geocomputation |
Comber L.,University of Leeds |
Sint H.,Rothamsted Research |
And 3 more authors.
Proceedings of Spatial Accuracy 2016 | Year: 2016
This study demonstrates a new approach for the visualization of spatial uncertainty using data from three agricultural field surveys.
Tarasov A.,Dublin Institute of Technology |
Kling F.,National Center for Geocomputation |
Pozdnoukhov A.,University of California at Berkeley
Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining | Year: 2013
Location-based social networks serve as a source of data for a wide range of applications, from recommendation of places to visit to modelling of city traffic, and urban planning. One of the basic problems in all these areas is the formulation of a predictive model for the location of a certain user at a certain time. In this paper, we propose a new approach for predicting user location, which uses two components to make the prediction, based on (i) coordinates and times of user check-ins and (ii) social interaction between different users. We improve the performance of a state-of-the art model using the radiation model of spatial choice and a social component based on the frequency of matching check-ins of user's friends. Friendship is defined by the presence of reciprocal following on Twitter. Our empirical results highlight an improvement over the state-of-the-art in terms of accuracy, and suggest practical solutions for spatio-temporal and socially-inspired prediction of user location. © 2013 ACM.
Coveney S.,University of Glasgow |
Coveney S.,National Center for Geocomputation
Photogrammetric Engineering and Remote Sensing | Year: 2013
Elevation error in the Intermap X-band airborne Interfero-metric Synthetic Aperture Radar DTM data set is defined in a 260-hectare mixed land-cover area using external dual-frequency GPS and bare-earth lidar point-cloud validation data. Absolute elevation error is reported globally, and within land-cover classes characterized by distinctive vegetation canopy densities and depths that are considered to have the potential to affect X-band DTM elevation error in distinctive ways. Observed global and land-cover specific elevation errors are subsequently compared with an external study where land-cover dependent errors were quantified within four lidar data sets that overlapped the IFSAR DTM validation area. The results of these absolute and comparative results are subsequently used to make recommendations regarding the potential of Intermap bare-earth IFSAR DTM data in environmental modeling applications elsewhere, and the scope for using the data in conjunction with, and as an alternative to airborne lidar data is discussed. © 2013 American Society for Photogrammetry and Remote Sensing.
Cahalane C.,National Center for Geocomputation |
McElhinney C.P.,National Center for Geocomputation |
Lewis P.,National Center for Geocomputation |
McCarthy T.,National Center for Geocomputation
Sensors (Switzerland) | Year: 2014
The current generation of Mobile Mapping Systems (MMSs) capture high density spatial data in a short time-frame. The quantity of data is difficult to predict as there is no concrete understanding of the point density that different scanner configurations and hardware settings will exhibit for objects at specific distances. Obtaining the required point density impacts survey time, processing time, data storage and is also the underlying limit of automated algorithms. This paper details a novel method for calculating point and profile information for terrestrial MMSs which are required for any point density calculation. Through application of algorithms utilising 3D surface normals and 2D geometric formulae, the theoretically optimal profile spacing and point spacing are calculated on targets. Both of these elements are a major factor in calculating point density on arbitrary objects, such as road signs, poles or buildings-all important features in asset management surveys. © 2014 by the authors; licensee MDPI, Basel, Switzerland.
Cahalane C.,National Center for Geocomputation |
McCarthy T.,National Center for Geocomputation |
McElhinney C.P.,National Center for Geocomputation
ACM International Conference Proceeding Series | Year: 2012
The current generation of Mobile Mapping Systems (MMSs) capture increasingly larger amounts of data in a short time frame. Due to the relative novelty of this technology there is no concrete understanding of the point density that different hardware configurations and operating parameters will exhibit on objects at specific distances. Depending on the project requirements, obtaining the required point density impacts on survey time, processing time, data storage and is the underlying limit of automated algorithms. A limited understanding of the capabilities of these systems means that defining point density in project specifications is a complicated process. We are in the process of developing a method for determining the quantitative resolution of point clouds collected by a MMS with respect to known objects at specified distances. We have previously demonstrated the capabilities of our system for calculating point spacing, profile angle and profile spacing individually. Each of these elements are a major factor in calculating point density on arbitrary objects, such as road signs, poles or buildings -all important features in asset management surveys. This paper will introduce the current version of the MobIle Mapping point densIty Calculator (MIMIC), MIMIC's visualisation module and finally discuss the methods employed to validate our work. © 2012 ACM.
Comber A.,University of Leicester |
Brunsdon C.,National Center for Geocomputation
International Journal of Climatology | Year: 2015
Changes in phenology are indicators of climate change. Urban land use influences local climates through mechanisms such as urban heat island (UHI) effects. This research examined the spatio-temporal variations in first flowering with changes in urban land use in England and Wales. It used standard ordinary least squares (OLSs) regressions and geographically weighted regressions (GWRs) to analyse changes in phenophase observation date between 1934 and 2007 for three tree species. The OLS models suggested that first flowering was getting earlier: blackthorn by 0.28 days per year, hawthorn by 0.16 days per year and horse chestnut by 0.13 days per year. These rates were found to vary spatially when GWR was used and the greatest rates of change were found to be highly localized. The addition of land use change was found to improve the model fit and suggested that a 10% increase in urban land use was also associated with phenophase advancement of 1.20 days for blackthorn, 0.57 days for hawthorn and 0.90 days for horse chestnut. When the impacts of urban land use changes was analysed using GWR, the associations with phenophase advancement were found to vary spatially, strongest associations were generally more pronounced in the north and especially in the extreme south-west and the north for all species. The results of this research suggest that the impacts of climate changes and the effects of urban land use changes on phenology vary spatially and that the impacts of urban expansion, such as UHI effects, may not be uniform. These findings suggest the need for spatially explicit analyses to quantify the local impacts and drivers of climate changes and their associated feedbacks. © 2014 Royal Meteorological Society.
Coveney S.,National Center for Geocomputation |
Stewart Fotheringham A.,National Center for Geocomputation
Photogrammetric Record | Year: 2011
Terrestrial laser scanning (TLS) data-sets are seeing increasing use in geology, geomorphology, forestry and urban mapping. The ease of use, affordability and operational flexibility of TLS suggest that demand for it is likely to increase in large-scale mapping studies. However, its advantages may remain restricted to specific environments, because of difficulties in defining bare-ground level in the presence of ground-level vegetation. This paper seeks to clarify the component contributions to TLS elevation error deriving from vegetation occlusion, scan co-registration error, point cloud georeferencing error and target position definition in TLS point cloud data. A multi-scan single-returns TLS point cloud data-set of very high resolution (∼250points/m2) was acquired for an 11 hectare area of open, substantially flat and 100% vegetated coastal saltmarsh, providing data for the empirical quantification of TLS error. Errors deriving from the sources discussed are quantified, clarifying the potential proportional contribution of vegetation to other error sources. Initial data validation is applied to the TLS point cloud data after application of a local-lowest-point selection process, and repeat validation tests are applied to the resulting filtered point cloud after application of a kriging-based error adjustment and data fusion with GPS. The final results highlight the problem of representing bare ground effectively within TLS data captured in the presence of dense ground vegetation and clarify the component contributions of elevation error deriving from surveying and data processing. © 2011 The Authors. The Photogrammetric Record © 2011 The Remote Sensing and Photogrammetry Society and Blackwell Publishing Ltd.