Infrastructure Engineering - Research Publications
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ItemD-Log: A WiFi Log-based differential scheme for enhanced indoor localization with single RSSI source and infrequent sampling rate(Elsevier, 2017-06-01)Currently, large amounts of Wi-Fi access logs are collected in diverse indoor environments, but cannot be widely used for fine-grained spatio-temporal analysis due to coarse positioning. We present a Log-based Differential (D-Log) scheme for post-hoc localization based on differentiated location estimates obtained from large-scale Access Point (AP) logs of WiFi connectivity traces, which can be used for data analysis and knowledge discovery of visitor behaviours. Specifically, the location estimates are calculated by utilizing a combination of Received Signal Strength Indicator (RSSI) records from two neighbouring APs. D-Log exploits real-world industry WiFi logs where RSSI data sampled at low rates from single AP sources are recorded in each connectivity trace. The approach is independent of device and network infrastructure type. D-Log is evaluated using WiFi logs collected from controlled environment as well as real-world uncontrolled public indoor spaces, which includes discrete single-AP RSSI traces of around 100,000 mobile devices over a one-year period. The experiment results indicate that, despite of the challenges with the infrequent sampling rate and the limitations of the data that only records RSSI from single AP sources in each instance, D-Log performs comparatively well to the state-of-the-art RSSI-based localization methods and presents a viable alternative for many application areas where high-accuracy positioning infrastructure may not be cost effective or where positioning applications are considered on legacy information infrastructure.
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ItemStreet Network Studies: from Networks to Models and their Representations(Springer Verlag, 2018-11-07)Over the last fifty years, research into street networks has gained prominence with a rapidly growing number of studies across disparate disciplines. These studies investigate a wide range of phenomena using a wealth of data and diverse analytical techniques. Starting within the fields of transport or infrastructure engineering, street networks have commonly been treated as sets of more or less homogeneous linear elements, connecting locations and intersecting at junctions. This view is commonly represented as a graph, which provides a common and rigorous formalisation accessible across disciplines and is particularly well-suited for problems such as flow optimisation and routing. Street networks are, however, complex objects of investigation and the way we model and then represent them as graphs has fundamental effects on the outcomes of a study. Many approaches to modelling street networks have been proposed, each lending itself to different analyses and supporting insights into diverse aspects of the urban system. Yet, this plurality and the relation between different models remains relatively obscure and unexplored. The motivations for adopting a given model of the network are also not always clear and often seem to follow disciplinary traditions. This paper provides an overview of key street network models and the prima facie merits of pertinent alternative approaches. It suggests greater attention to consistent use of terms and concepts, of graph representations and practical applications, and concludes with suggestions for possible ways forward.
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ItemDiscovery of topological constraints on spatial object classes using an extended topological model(University of Maine, 2019)In a typical data collection process, a surveyed spatial object is annotated upon creation, and is classified based on its attributes. This annotation can also be guided by textual definitions of objects. However, interpretations of such definitions may differ among people, and thus result in subjective and inconsistent classification of objects. This problem becomes even more pronounced if the cultural and linguistic differences are considered. As a solution, this paper investigates the role of topology as the defining characteristic of a class of spatial objects. We propose a data mining approach based on frequent itemset mining to learn patterns in topological relations between objects of a given class and other spatial objects. In order to capture topological relations between more than two (linear) objects, this paper further proposes an extension of the 9-intersection model for topological relations of line geometries. The discovered topological relations form topological constraints of an object class that can be used for spatial object classification. A case study has been carried out on bridges in the OSM dataset for the state of Victoria, Australia. The results show that the proposed approach can successfully learn topological constraints for the class bridge, and that the proposed extended topological model for line geometries outperforms the 9-intersection model in this task.
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ItemInitial Analysis of Simple Where-Questions and Human-Generated Answers(LIPIcs, 2019)Geographic questions are among the most frequently asked questions in Web search and question answering systems. While currently responses to the questions are machine-generated by document/snippet retrieval, in the future these responses will need to become more similar to answers provided by humans. Here, we have analyzed human answering behavior as response to simple where questions (i.e., where questions formulated only with one toponym) in terms of type, scale, and prominence of the places referred to. We have used the largest available machine comprehension dataset, MS-MARCO v2.1. This study uses an automatic approach for extraction, encoding and analysis of the questions and answers. Here, the distribution analysis are used to describe the relation between questions and their answers. The results of this study can inform the design of automatic question answering systems for generating useful responses to where questions.
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ItemWhat can urban mobility data reveal about the spatial distribution of infection in a single city?(BioMed Central, 2019-05-29)Background Infectious diseases spread through inherently spatial processes. Road and air traffic data have been used to model these processes at national and global scales. At metropolitan scales, however, mobility patterns are fundamentally different and less directly observable. Estimating the spatial distribution of infection has public health utility, but few studies have investigated this at an urban scale. In this study we address the question of whether the use of urban-scale mobility data can improve the prediction of spatial patterns of influenza infection. We compare the use of different sources of urban-scale mobility data, and investigate the impact of other factors relevant to modelling mobility, including mixing within and between regions, and the influence of hub and spoke commuting patterns. Methods We used journey-to-work (JTW) data from the Australian 2011 Census, and GPS journey data from the Sygic GPS Navigation & Maps mobile app, to characterise population mixing patterns in a spatially-explicit SEIR (susceptible, exposed, infectious, recovered) meta-population model. Results Using the JTW data to train the model leads to an increase in the proportion of infections that arise in central Melbourne, which is indicative of the city’s spoke-and-hub road and public transport networks, and of the commuting patterns reflected in these data. Using the GPS data increased the infections in central Melbourne to a lesser extent than the JTW data, and produced a greater heterogeneity in the middle and outer regions. Despite the limitations of both mobility data sets, the model reproduced some of the characteristics observed in the spatial distribution of reported influenza cases. Conclusions Urban mobility data sets can be used to support models that capture spatial heterogeneity in the transmission of infectious diseases at a metropolitan scale. These data should be adjusted to account for relevant urban features, such as highly-connected hubs where the resident population is likely to experience a much lower force of infection that the transient population. In contrast to national and international scales, the relationship between mobility and infection at an urban level is much less apparent, and requires a richer characterisation of population mobility and contact.
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ItemUnderstanding the predictability of user demographics from cyber-physical-social behaviours in indoor retail spaces(Springer, 2018-07-06)Understanding the association between customer demographics and behaviour is critical for operators of indoor retail spaces. This study explores such an association based on a combined understanding of customer Cyber (online), Physical, and (some aspects of) Social (CPS) behaviour, at the conjunction of corresponding CPS spaces. We combine the results of a traditional questionnaire with large-scale WiFi access logs, which capture customer cyber and physical behaviour. We investigate the predictability of user demographics based on CPS behaviors captured from both sources. We find (1) strong correlations between users’ demographics and their CPS behaviors; (2) log-recorded cyber-physical behavior reflects well data captured in the corresponding questionnaire; (3) different CPS behaviors contribute differently to the predictability of demographic attributes; and (4) the predictability of user demographics from logs is comparable to questionnaire-based data. As such, our study provides strong support for demographic studies based on large-scale logs data capture.