Investigating the performance of transport infrastructure using real-time data and a scalable multi-modal agent based model

The idea that including more information in more dynamic and iterative ways is central to the promise of the big data paradigm. The hope is that via new data sources, such as remote sensors and mobile phones, the reliance on heavily simplified generalised functions for model inputs will be erased. This trade between idealised and actual empirical data will be matched with dynamic models which consider complexity at a fundamental level, inherently mirroring the systems they are attempting to replicate. Cloud computing brings the possibility of doing all of this, in less time than the simplified macro models of the past, thus enabling better answers and at the time of critical decision making junctures. This research was task driven - the question of high speed rail versus aviation led to an investigation into the simplifications and assumptions that back up many of the commonly held beliefs on the sustainability of different modes of transport. The literature ultimately highlighted the need for context specific information; actual load factors, actual journey times considering traffic/engineering works and so on. Thus, rather than being explicitly an exercise in answering a specific question, a specific question was used to drive the development of a tool which may hold promise for answering a range of transportation related questions. The original contributions of this work are, firstly the use of real-time data sources to quantify temporally and spatially dynamic network performance metrics (eg. journey times on different transport models) and secondly to organise these data sources in a framework which can handle the volume and type of the data and organise the data in a way so that it is useful for the dynamic agent based modelling of future scenarios.EPSRC I Case Studentship with Ove Arup & Partner

Faces in the crowd:Twitter as alternative to protest surveys

Who goes to protests? To answer this question, existing research has relied either on retrospective surveys of populations or in-protest surveys of participants. Both techniques are prohibitively costly and face logistical and methodological constraints. In this article, we investigate the possibility of surveying protests using Twitter. We propose two techniques for sampling protestors on the ground from digital traces and estimate the demographic and ideological composition of ten protestor crowds using multidimensional scaling and machine-learning techniques. We test the accuracy of our estimates by comparing to two in-protest surveys from the 2017 Women’s March in Washington, D.C. Results show that our Twitter sampling techniques are superior to hashtag sampling alone. They also approximate the ideology and gender distributions derived from on-the-ground surveys, albeit with some bias, but fail to retrieve accurate age group estimates. We conclude that online samples are yet unable to provide reliable representative samples of offline protest

Association of violence with urban points of interest

The association between alcohol outlets and violence has long been recognised, and is commonly used to inform policing and licensing policies (such as staggered closing times and zoning). Less investigated, however, is the association between violent crime and other urban points of interest, which while associated with the city centre alcohol consumption economy, are not explicitly alcohol outlets. Here, machine learning (specifically, LASSO regression) is used to model the distribution of violent crime for the central 9 km2 of ten large UK cities. Densities of 620 different Point of Interest types (sourced from Ordnance Survey) are used as predictors, with the 10 most explanatory variables being automatically selected for each city. Cross validation is used to test generalisability of each model. Results show that the inclusion of additional point of interest types produces a more accurate model, with significant increases in performance over a baseline univariate alcohol-outlet only model. Analysis of chosen variables for city-specific models shows potential candidates for new strategies on a per-city basis, with combined-model variables showing the general trend in POI/violence association across the UK. Although alcohol outlets remain the best individual predictor of violence, other points of interest should also be considered when modelling the distribution of violence in city centres. The presented method could be used to develop targeted, city-specific initiatives that go beyond alcohol outlets and also consider other locations

Estimating Trail Use and Visitor Spatial Distribution Using Mobile Device Data: An Example From the Nature Reserve of Orange County, California USA

Monitoring visitor use in parks and protected areas (PPAs) provides essential information for managers of PPAs to evaluate aspects of the visitor experience and balance the ecological disturbance that use creates. Traditional methods for quantifying visitation and spatial use of PPAs are resource intensive and thus are conducted infrequently or at cost-effective intervals which may fail to capture the dynamic nature of modern visitor use trends. This paper provides an addition to a growing literature using mobile-device data to quantify visitation and spatial density of use of urban-proximate PPAs in Orange County, California, USA using the analysis platform Streetlight, Inc. The results of our analysis compared favorably with well-established automatic trail counting and GPS-based monitoring methods, and illustrate several advantages of mobile device data to inform the management of PPAs. Mobile device data provide reliable estimates of visitation and spatial density of use and can augment and compliment existing social and resource monitoring for PPA management and planning

Planning for green infrastructure : the spatial effects of parks, forests, and fields on Helsinki's apartment prices

As the importance of urban green spaces is increasingly recognised, so does the need for their systematic placement in a broader array of socioeconomic objectives. From an urban planning and economics perspective, this represents a spatial task: if more land is allocated to various types of green, how do the economic effects propagate throughout urban space? This paper focuses on the spatial marginal effects of forests, parks, and fields and estimates spatial hedonic models on a sample of apartment transactions in Helsinki, Finland. The results indicate that the capitalization of urban green in apartment prices depends on the type of green, but also interacts with distance to the city centre. Additionally, the effects contain variable pure and spatial spillover impacts, also conditional on type and location, the separation of which highlights aspects not commonly accounted for. The planning of green infrastructure will therefore benefit from parameterizing interventions according to location, green type, and character of spatial impacts.Peer reviewe

A machine learning-guided data integration framework to measure multidimensional poverty

As developing nations like South Africa chart a path of socio-economic development, the spatialisation of progress, opportunity, and neglect is a critical antecedent to policy-making and regional interventionism. Efforts to capture meaningful data using household surveys and censuses face a diluted accuracy due to sampling, surveying, and quantification errors. The reliability and regularity of these traditional methods is also constrained since the processes are costly and time consuming. Recent investigations in the field of machine learning and satellite imaging have presented a viable proof-of-concept technique to exploit specific economic indicators to demonstrate economic development patterns across regional areas. The current study adopts several interrelated approaches encompassed within the field of remote sensing in order to evaluate and model poverty in the South African landscape. By adopting publicly accessible information for classification to indicate the intensity of poverty, this study proposed an inexpensive solution to poverty estimation. Concretely, the solution combined satellite imagery and geospatial data with regional poverty data exploiting an ensemble approach to poverty diagnosis. The solution is based upon multidimensional indicators and multi-layered insights that can be extrapolated from overlapping models to bolster them and help with socio-economic well-being estimations. Through machine learning techniques and object-oriented training of a convolutional neural network, this study revealed that a naïve combination of distinct data sources shows patterns of socio-economic well-being in South Africa by achieving an R2 of 0.56 wealth estimation compared to 0.54 from satellite imagery. This outlined variability and incongruity within landscapes that not only reflect the persistent subdivisions of apartheid-era enclavisation, but indicate critical gaps in domestic social services, infrastructure, and developmental pathways. This study is applicable to policy makers in low- and middle-income countries that lack accurate and timely data on economic development as an important precursor to public support, policy making, and planned expenditures.School of ComputingM. Tech. (Information Technology