Probabilistic latent semantic analysis as a potential method for integrating spatial data concepts

In this paper we explore the use of Probabilistic Latent Semantic Analysis (PLSA) as a method for quantifying semantic differences between land cover classes. The results are promising, revealing ‘hidden’ or not easily discernible data concepts. PLSA provides a ‘bottom up’ approach to interoperability problems for users in the face of ‘top down’ solutions provided by formal ontologies. We note the potential for a meta-problem of how to interpret the concepts and the need for further research to reconcile the top-down and bottom-up approaches

The impact of contributor confidence, expertise and distance on the crowdsourced land cover data quality

There is much interest in the opportunities for formal scientific investigations afforded by crowdsourcing and citizen sensing activities. However, one of the critical research issues relates to the 'quality' of the data collected in this way. This paper uses volunteer data on land cover collected under the Geo-Wiki system, where contributors label the land cover class at a series of locations, with expert labels at the same locations. It examines the statistical relationships between the accuracy of volunteer labels, their self assessed confidence in labeling, their 'experiential distance' to the location under consideration and the level of their domain expertise. The results show that distance has a minor effect on the reliability of land cover labeling, and that generally expertise has a greater effect, but not for all landcover classes

Using graph structural information about flows to enhance short-term demand prediction in bike-sharing systems

Short-term demand prediction is important for managing transportation infrastructure, particularly in times of disruption, or around new developments. Many bike-sharing schemes face the challenges of managing service provision and bike fleet rebalancing due to the “tidal flows” of travel and use. For them, it is crucial to have precise predictions of travel demand at a fine spatiotemporal granularities. Despite recent advances in machine learning approaches (e.g. deep neural networks) and in short-term traffic demand predictions, relatively few studies have examined this issue using a feature engineering approach to inform model selection. This research extracts novel time-lagged variables describing graph structures and flow interactions from real-world bike usage datasets, including graph node Out-strength, In-strength, Out-degree, In-degree and PageRank. These are used as inputs to different machine learning algorithms to predict short-term bike demand. The results of the experiments indicate the graph-based attributes to be more important in demand prediction than more commonly used meteorological information. The results from the different machine learning approaches (XGBoost, MLP, LSTM) improve when time-lagged graph information is included. Deep neural networks were found to be better able to handle the sequences of the time-lagged graph variables than other approaches, resulting in more accurate forecasting. Thus incorporating graph-based features can improve understanding and modelling of demand patterns in urban areas, supporting bike-sharing schemes and promoting sustainable transport. The proposed approach can be extended into many existing models using spatial data and can be readily transferred to other applications for predicting dynamics in mass transit systems. A number of limitations and areas of further work are discussed

A spatiotemporal and graph-based analysis of dockless bike sharing patterns to understand urban flows over the last mile

The recent emergence of dockless bike sharing systems has resulted in new patterns of urban transport. Users can begin and end trips from their origin and destination locations rather than docking stations. Analysis of changes in the spatiotemporal availability of such bikes has the ability to provide insights into urban dynamics at a finer granularity than is possible through analysis of travel card or dock-based bike scheme data. This study analyses dockless bike sharing in Nanchang, China over a period when a new metro line came into operation. It uses spatial statistics and graph-based approaches to quantify changes in travel behaviours and generates previously unobtainable insights about urban flow structures. Geostatistical analyses support understanding of large-scale changes in spatiotemporal travel behaviours and graph-based approaches allow changes in local travel flows between individual locations to be quantified and characterized. The results show how the new metro service boosted nearby bike demand, but with considerable spatial variation, and changed the spatiotemporal patterns of bike travel behaviour. The analysis also quantifies the evolution of travel flow structures, indicating the resilience of dockless bike schemes and their ability to adapt to changes in travel behaviours. More widely, this study demonstrates how an enhanced understanding of urban dynamics over the “last-mile” is supported by the analyses of dockless bike data. These allow changes in local spatiotemporal interdependencies between different transport systems to be evaluated, and support spatially detailed urban and transport planning. A number of areas of further work are identified to better to understand interdependencies between different transit system components

Regionally-structured explanations behind area-level populism: An update to recent ecological analyses

Heavy geographic patterning to the 2016 Brexit vote in UK and Trump vote in US has resulted in numerous ecological analyses of variations in area-level voting behaviours. We extend this work by employing modelling approaches that permit regionally-specific associations between outcome and explanatory variables. We do so by generating a large number of regional models using penalised regression for variable selection and coefficient evaluation. The results reinforce those already published in that we find associations in support of a ‘left-behind’ reading. Multivariate models are dominated by a single variable—levels of degree-education. Net of this effect, ‘secondary’ variables help explain the vote, but do so differently for different regions. For Brexit, variables relating to material disadvantage, and to a lesser extent structural-economic circumstances, are more important for regions with a strong industrial history than for regions that do not share such a history. For Trump, increased material disadvantage reduces the vote both in global models and models built mostly for Southern states, thereby undermining the ‘left-behind’ reading. The reverse is nevertheless true for many other states, particularly those in New England and the Mid-Atlantic, where comparatively high levels of disadvantage assist the Trump vote and where model outputs are more consistent with the UK, especially so for regions with closer economic histories. This pattern of associations is exposed via our regional modelling approach, application of penalised regression and use of carefully designed visualization to reason over 100+ model outputs located within their spatial context. Our analysis, documented in an accompanying github repository, is in response to recent calls in empirical Social and Political Science for fuller exploration of subnational contexts that are often controlled out of analyses, for use of modelling techniques more robust to replication and for greater transparency in research design and methodology

Urban planning, public participation and digital technology: App development as a method of generating citizen involvement in local planning processes

There has been a recent shift in England towards empowering citizens to shape their neighbourhoods. However, current methods of participation are unsuitable or unwieldy for many people. In this paper, we report on ChangeExplorer, a smart watch application to support citizen feedback, to investigate the extent to which digital wearables can address barriers to participation in planning. The research contributes to both technology-mediated citizen involvement and urban planning participation methods. The app leverages in-situ, quick interactions encouraging citizens to reflect and comment on their environment. Taking a case study approach, the paper discusses the design and deployment of the app in a local planning authority through interviews with 19 citizens and three professional planners. The paper discusses the potential of the ChangeExplorer app to address more conceptual issues, and concludes by assessing the degree to which the technology raises awareness of urban change and whether it could serve as a gateway to more meaningful participatory methods

Geographically weighted correspondence matrices for local error reporting and change analyses: mapping the spatial distribution of errors and change

This letter describes and applies generic methods for generating local measures from the correspondence table. These were developed by integrating the functionality of two existing R packages: gwxtab and diffeR. They demonstrate how spatially explicit accuracy and error measures can be generated from local geographically weighted correspondence matrices, for example to compare classified and reference data (predicted and observed) for error analyses, and classes at times t1 and t2 for change analyses. The approaches in this letter extend earlier work that considered the measures derived from correspondence matrices in the context of generalized linear models and probability. Here the methods compute local, geographically weighted correspondence matrices, from which local statistics are directly calculated. In this case a selection of the overall and categorical difference measures proposed by Pontius and Milones (2011) and Pontius and Santacruz (2014), as well as spatially distributed estimates of kappa coefficients, User and Producer accuracies. The discussion reflects on the use of the correspondence matrix in remote sensing research, the philosophical underpinnings of local rather than global approaches for modelling landscape processes and the potential for policy and scientific benefits that local approaches support

Developmental toxicity of metaldehyde in the embryos of Lymnaea stagnalis (Gastropoda: Pulmonata) co-exposed to the synergist piperonyl butoxide.

Metaldehyde is a widely used molluscicide in countries where damage to crops from slugs and snails is a major problem associated with warm and wet winters. In the UK it is estimated that over 8% of the area covered by arable crops is treated with formulated granular bait pellets containing metaldehyde as the main active ingredient. Metaldehyde is hydrophilic (log Kow=0.12), water soluble (200 mg·L(-1) at 17 °C) and has been detected in UK surface waters in the concentration range of typically 0.2-0.6 μg·L(-1) (maximum 2.7 μg·L(-1)) during 2008-2011. In the absence of chronic data on potential hazards to non-target freshwater molluscs, a laboratory study was conducted to investigate the impact of metaldehyde on embryo development in the gastropod Lymnaea stagnalis (RENILYS strain) and using zinc as a positive control. L. stagnalis embryos were exposed to metaldehyde under semi-static conditions at 20±1 °C and hatching success and growth (measured as shell height and intraocular distance) examined after 21 d. Exposure concentrations were verified using HPLC and gave 21 d (hatching)NOEC and (hatching)LOEC mean measured values of 36 and 116 mg MET·L(-1), respectively (equal to the 21 d (shell height)NOEC and (shell height)LOEC values). For basic research purposes, a second group of L. stagnalis embryos was co-exposed to metaldehyde and the pesticide synergist piperonyl butoxide (PBO). Co-exposure to the PBO (measured concentrations between 0.47-0.56 mg·L(-1)) reduced hatching success from 100% to 47% and resulted in a 30% reduction in embryo growth (shell height) in snail embryos co-exposed to metaldehyde at 34-36 mg·L(-1) over 21 d. In conclusion, these data suggest mollusc embryos may have some metabolic detoxication capacity for metaldehyde and further work is warranted to explore this aspect in order to support the recent initiative to include molluscs in the OECD test guideline programme

Proposed Environmental Quality Standards for Phenol in Water

This is the Proposed Environmental Quality Standards (EQS) for Phenol in Water prepared for the National Rivers Authority, and published by the Environment Agency in 1995. The report reviews the properties and uses of phenol, its fate, behaviour and reported concentrations in the environment and critically assesses the available data on its toxicity and bioaccumulation. The information is used to derive EQSs for the protection of fresh and saltwater life and for the abstraction of water to potable supply. Phenol is widely used as a chemical intermediate and the main sources for phenol in the environment are of anthropogenic origin. Phenol may also be formed during natural decomposition of organic material. The persistence of phenol in the aquatic environment is low with biodegradation being the main degradation process (half-lives of hours to days). Phenol is moderately toxic to aquatic organisms and its potential to bioaccumulate in aquatic organisms is low