Recently, major cities are facing air pollution problems mostly caused by individual car traffic. Besides the emission of greenhouse gases, particulate matter is a particular concern for public health. In order to mitigate these emission related issues, we developed an environmentally friendly routing approach, which calculates the most fuel-efficient route - based on the driving dynamics of the road, vehicle, and traffic characteristics. In addition, the calculated route is designed to avoid regions of high particulate matter concentration. In order to integrate real-time air quality data of moving and stationary sensors using OGC Sensor Observation Service. Cars are used as moving sensors in the city. The paper evaluates the effects of air quality (particulate matter & greenhouse gases) on the route calculation - so that cars/bikes may receive real-time recommendations to avoid polluted areas.
Geographically weighted Poisson regression (GWPR) is widely used for spatial regression analysis of count data. However, it tends to be unstable because of a fundamental drawback of Poisson regression. To overcome the drawback, we introduce a log-linear approximation to estimate GWPR without relying on Poisson regression framework. The proposed approach approximates GWPR using the basic GWR modeling with transformed explained variables. Monte Carlo experiments show that the proposed GWPR outperforms the conventional GWPR in terms of both estimation accuracy and computationally efficiency. Finally, the proposed GWPR is applied to an analysis of coronavirus disease 2019 (COVID-19).
For human-centered mobile navigation systems, a computational landmark selection model is critical to automatically include landmarks for communicating routes with users. Although some empirical studies have shown that landmarks selected by familiar and unfamiliar wayfinders, respectively, differ significantly, existing computational models are solely focused on unfamiliar users and ignore selecting landmarks for familiar users, particularly in indoor environments. Meanwhile, it is unclear how the importance of salience metrics employed by machine learning approaches differs from that reported by human participants during landmark selection. In this study, we propose a LambdaMART-based ranking approach to computationally modelling indoor landmark selection. Two models, one for familiar and one for unfamiliar users, respectively, were trained from the human-labelled indoor landmark selection data. The importance of different salience measures in each model was then ranked and compared with human participants’ self-report results of a survey. The evaluation results demonstrate that familiarity does indeed matter in the computational modelling of indoor landmark selection. The ranking differences of salience measures in the trained models show that the salience varies with the familiarity of wayfinders. Moreover, the calculated intraclass correlation coefficients (0.62 for familiar, 0.65 for unfamiliar) illustrate the median consistency between the computational results on feature importance and the self-reported importance results by human participants, confirming the reliability and interpretability of the proposed approach.
Accurate and up-to-date land use maps are important to the study of human-environment interactions, urban morphology, environmental justice, etc. Traditional land use mapping approaches involve several surveys and expert knowledge of the region to be mapped. While traditional approaches generate accurate and authoritative maps, it is expensive and takes a long time to develop a new version of map. Besides, such maps have region-specific spatial embedding, making them difficult to benchmark and compare against other land use maps. This work introduces a scalable POI-based land use modeling approach to generate global land use maps at multiple spatial scales and different semantic granularities. In addition, our land use maps adhere to a unified land use categories and can be compared for accuracy and precision.
This paper describes the novel development and application of a multi-scale geographically weighted discriminant analysis (MSGWDA). This is applied to a case study of survey data of attitudes to a proposed motorbike / scooter ban in Han Noi, Vietnam. It uses discriminant analysis to examine attitudes to the ban in relation to travel purposes, distances, respondent age and so on. The main part of the paper focuses on describing the novel MSGWDA approach, and the results indicate the varying scales of relationship between the different input variables and the categorical responses variable. The paper also reflects on the pervasive logic of the approaches used to fit multiscale geographically weighted bandwidths (for example in regression). These have historically been based on the iterative back-fitting approaches used in GAMs, but risk missing potentially important variable interactions amongst un-evaluated bandwidths because of the sequence of their application. It is argued that although pragmatic in the 1990s, it may be possible to apply more deterministic approaches with increased memory and readily accessible computing power in order to better navigate such highly dimensional search spaces.
The use of machine learning models (ML) in spatial statistics and urban analytics is increasing. However, research studying the generalisability of ML models from a geographical perspective had been sparse, specifically on whether a model trained in one context can be used in another. The aim of this research is to explore the extent to which standard models such as convolutional neural networks being applied on urban images can generalise across different geographies, through two tasks. First, on the classification of street frontages and second, on the prediction of real estate values. In particular, we find in both experiments that the models do not generalise well. More interestingly, there are also differences in terms of generalisability within the first case study which needs further exploration. To summarise, our results suggest that in urban analytics there is a need to systematically test out-of-geography results for this type of geographical image-based models.
Designing a Geospatial Question Answering (GeoQA) system that takes a user’s GIS-related domain question, understands how to gather the required data, how to analyse it, and how to present the results in a suitable format is arguably among the most important “moonshots” in the GeoAI field. In this study, we focus specifically on answering geo-event questions. This work begins by presenting a prototype process for generating workflows to answer geo-event questions by providing annotations of the domain, comprising a tool taxonomy we created from descriptions of geo-operations, a data type ontology obtained from the Core Concept Data types (CCD) ontology, and the annotations of the mentioned geo-operations with respect to the input/output pairs. Finally, the generated workflows are post-processed to restrict the solution space and provide more structured solutions. The results of this research provide a step towards the implementation of a geo-event QA system capable of answering diverse geo-event questions defined by users.
Public transportation in cities is less popular than the private car due to lower personal flexibility, perceived comfort or the unavailability of infrastructure. The latter one is an issue in Augsburg with regard to outer districts since the existing star-shaped network layout requires a route through the inner city. A recent proposal called "Verkehr4.0" aims to extend the layout of the existing infrastructure by adding new express bus lines to connect outer city districts. This research paper investigates the direct traffic flow between the outer districts Stadtbergen and Göggingen in contrast to the existing flow via the central hub "Königsplatz". We implement an agent-based simulation comparing waiting times, travel times and total times spent on trips in the two scenarios. Furthermore, we model a measure dubbed "happiness" of the people as well as their willingness to change their mode of transport. The preliminary results of our simulation show that waiting time for public transport users decreases, while total time, travel time and happiness reveal no statistical difference through the introduction of an express bus line.
Data related to households or addresses needs be published in an aggregated form to obfuscate sensitive information about individuals. Usually, the data is aggregated to the level of existing administrative zones, but these often do not correspond to formal models of privacy or a desired level of anonymity. Therefore, automatic privacy-preserving spatial clustering methods are needed. To address this need, we present algorithms to partition a given set of locations into k-anonymous clusters, meaning that each cluster contains at least k locations. We assume that the locations are given as a set T ⊆ V of terminals in a weighted graph G = (V, E) representing a road network. Our approach is to compute a forest in G, i.e., a set of trees, each of which corresponds to a cluster. We ensure the k-anonymity of the clusters by constraining the trees to span at leastterminals each (plus an arbitrary number of non-terminal nodes called Steiner nodes). By minimizing the total edge weight of the forest, we ensure that the clusters reflect the proximity among the locations. Although the problem is NP-hard, we were able to solve instances of several hundreds of terminals using integer linear programming. Moreover, we present an efficient approximation algorithm and show that it can be used to process large and fine-grained data sets.
In the city of Hanoi, Vietnam, as with other rapidly-developing cities, transport infrastructure is failing to keep pace with the burgeoning population. This has lead to high levels of congestion, air pollution, and a broad inequity in the accessibility of large parts of the city to residents. The emerging discipline of Urban Data Science has a valuable role in providing policy makers with robust evidence on which to base policy, but the discipline faces problems with the application of techniques that are based on assumptions that do not hold when applied to emerging economies.
This paper presents the preliminary outputs of a new programme of urban data science work that is being developed specifically for Hanoi. It leverages a spatial microsimulation approach to up-sample a bespoke travel survey and create a synthetic representation of the transport preferences of all residents in the city. These new data are used to assess the impacts that changes in the broader socio-economic context, such as increasing prosperity amongst residents, could have on rates of car ownership and hence on the problems of congestion and pollution. The results begin to highlight parts of the city where the impacts of improved economic conditions coupled with changes to wider transport policies might lead to greater use of personal cars in the future.
