Exploring the potential of technology in enabling the inclusive co-production of space

The potential of emerging technology to address poly-urban issues is a growing focus on the agendas of cities worldwide. However, there is a lack of consensus regarding how and in whose interests it should be applied - should the aim be to establish 'smart cities' or to encourage 'smart citizens'? The 'bottom-up' approach advocates the latter and recognises the potential of technology to facilitate the prioritisation of issues and co-production of spaces. Particularly in a developing context where resources are severely limited, the ability to prioritise interventions to have maximum impact is exciting. However, these projects and the processes which enable them are under-researched. In this dissertation, a combination of Network Action Research and case study methods are used to guide the application of a selection of digital tools in combination with semi-structured and indepth interviews, surveys, and focus groups to a specific context. The products of this are insights regarding the processes which enable inclusive bottom-up smart city projects; the application of the Network Action Research method; and a context-specific resource of information to guide the future prioritisation of projects and planning in the study area. This dissertation explores the value of inclusive participation in planning, and the role that technology can play in facilitating this. However, it also uncovers the complex and non-linear nature of these projects, ultimately arguing that although technology is a valuable resource, it is not a catch-all. A hybridised approach to bottom-up smart city projects is crucial to their success

Developing a Computer Vision-Based Decision Support System for Intersection Safety Monitoring and Assessment of Vulnerable Road Users

Vision-based trajectory analysis of road users enables identification of near-crash situations and proactive safety monitoring. The two most widely used sur-rogate safety measures (SSMs), time-to-collision (TTC) and post-encroachment time (PET)—and a recent variant form of TTC, relative time-to-collision (RTTC)—were investigated using real-world video data collected at ten signalized intersections in the city of San Diego, California. The performance of these SSMs was compared for the purpose of evaluating pedestrian and bicyclist safety. Prediction of potential trajectory intersection points was performed to calculate TTC for every interacting object, and the average of TTC for every two objects in critical situations was calculated. PET values were estimated by observing potential intersection points, and frequencies of events were estimated in three critical levels. Although RTTC provided useful information regarding the relative distance between objects in time, it was found that in certain conditions where objects are far from each other, the interaction between the objects was incorrectly flagged as critical based on a small RTTC. Comparison of PET, TTC, and RTTC for different critical classes also showed that several interactions were identified as critical using one SSM but not critical using a different SSM. These findings suggest that safety evaluations should not solely rely on a single SSM, and instead a combination of different SSMs should be considered to ensure the reliability of evaluations. Video data analysis was conducted to develop object detection and tracking models for automatic identification of vehicles, bicycles, and pedestrians. Outcomes of machine vision models were employed along with SSMs to build a decision support system for safety assessment of vulnerable road users at signalized intersections. Promising results from the decision support system showed that automated safety evaluations can be performed to proactively identify critical events. It also showed challenges as well as future directions to enhance the performance of the system

Brooklyn of Korea: Place branding as a process in production of space

In planning and development practices, branding is often used as a promotional tool to attract investments and tourists, and thought of as a mechanism to portray a selected image of a place. In this thesis, I argue that the branding process can be one of the driving forces of neighborhood change and that place brands play an active role in producing sense of place along with physical and social changes. As cities increasingly choose images to communicate outwards and reposition themselves after the decline of industry, it is important to understand the role place brands play in the production and transformation of space. This thesis examines a neighborhood in transition, Seongsu-dong, Seoul, South Korea. From being one of Seoul’s few semi-industrial zones to a “hot place” for cultural and commercial activities, Seongsu has seen large shifts in the past decade, widely branded with the label “Brooklyn of Korea.” With diverse parties using the Brooklyn brand in different ways while leveraging similar qualities, Seongsu provides a rich case study on how branding as a process not only shapes images of a place, but can also impact the built environment. Through qualitative and quantitative analysis, this thesis tries to bridge the gap between portrayal of neighborhood change and tangible changes and answers: How are place brands created? What are the brands and how do they relate to neighborhood change? And what can place brands tell urban planners about neighborhood change?M.C.P

Mobile phone technology as an aid to contemporary transport questions in walkability, in the context of developing countries

The emerging global middle class, which is expected to double by 2050 desires more walkable, liveable neighbourhoods, and as distances between work and other amenities increases, cities are becoming less monocentric and becoming more polycentric. African cities could be described as walking cities, based on the number of people that walk to their destinations as opposed to other means of mobility but are often not walkable. Walking is by far the most popular form of transportation in Africa’s rapidly urbanising cities, although it is not often by choice rather a necessity. Facilitating this primary mode, while curbing the growth of less sustainable mobility uses requires special attention for the safety and convenience of walking in view of a Global South context. In this regard, to further promote walking as a sustainable mobility option, there is a need to assess the current state of its supporting infrastructure and begin giving it higher priority, focus and emphasis. Mobile phones have emerged as a useful alternative tool to collect this data and audit the state of walkability in cities. They eliminate the inaccuracies and inefficiencies of human memories because smartphone sensors such as GPS provides information with accuracies within 5m, providing superior accuracy and precision compared to other traditional methods. The data is also spatial in nature, allowing for a range of possible applications and use cases. Traditional inventory approaches in walkability often only revealed the perceived walkability and accessibility for only a subset of journeys. Crowdsourcing the perceived walkability and accessibility of points of interest in African cities could address this, albeit aspects such as ease-of-use and road safety should also be considered. A tool that crowdsources individual pedestrian experiences; availability and state of pedestrian infrastructure and amenities, using state-of-the-art smartphone technology, would over time also result in complete surveys of the walking environment provided such a tool is popular and safe. This research will illustrate how mobile phone applications currently in the market can be improved to offer more functionality that factors in multiple sensory modalities for enhanced visual appeal, ease of use, and aesthetics. The overarching aim of this research is, therefore, to develop the framework for and test a pilot-version mobile phone-based data collection tool that incorporates emerging technologies in collecting data on walkability. This research project will assess the effectiveness of the mobile application and test the technical capabilities of the system to experience how it operates within an existing infrastructure. It will continue to investigate the use of mobile phone technology in the collection of user perceptions of walkability, and the limitations of current transportation-based mobile applications, with the aim of developing an application that is an improvement to current offerings in the market. The prototype application will be tested and later piloted in different locations around the globe. Past studies are primarily focused on the development of transport-based mobile phone applications with basic features and limited functionality. Although limited progress has been made in integrating emerging advanced technologies such as Augmented Reality (AR), Machine Learning (ML), Big Data analytics, amongst others into mobile phone applications; what is missing from these past examples is a comprehensive and structured application in the transportation sphere. In turn, the full research will offer a broader understanding of the iii information gathered from these smart devices, and how that large volume of varied data can be better and more quickly interpreted to discover trends, patterns, and aid in decision making and planning. This research project attempts to fill this gap and also bring new insights, thus promote the research field of transportation data collection audits, with particular emphasis on walkability audits. In this regard, this research seeks to provide insights into how such a tool could be applied in assessing and promoting walkability as a sustainable and equitable mobility option. In order to get policy-makers, analysts, and practitioners in urban transport planning and provision in cities to pay closer attention to making better, more walkable places, appealing to them from an efficiency and business perspective is vital. This crowdsourced data is of great interest to industry practitioners, local governments and research communities as Big Data, and to urban communities and civil society as an input in their advocacy activities. The general findings from the results of this research show clear evidence that transport-based mobile phone applications currently available in the market are increasingly getting outdated and are not keeping up with new and emerging technologies and innovations. It is also evident from the results that mobile smartphones have revolutionised the collection of transport-related information hence the need for new initiatives to help take advantage of this emerging opportunity. The implications of these findings are that more attention needs to be paid to this niche going forward. This research project recommends that more studies, particularly on what technologies and functionalities can realistically be incorporated into mobile phone applications in the near future be done as well as on improving the hardware specifications of mobile phone devices to facilitate and support these emerging technologies whilst keeping the cost of mobile devices as low as possible

An automated approach to enrich OpenStreetMap data on footways

Urbanization and the rising global life expectancy are shaping the 21st century, and an increasing number of the older and disabled population is expected, emphasizing the need of developing age-friendly and accessible cities for all. The disabled population encounters barriers in accessing public services that able-bodied people do not, especially on footways. OpenStreetMap (OSM) data is applied in many routing applications for disabled people but does still lack a considerable amount of accessibility information, for example, only less than 2% of OSM footpaths in the city of Zurich contain inclination information. This thesis aims to enrich OSM footpaths in the city of Zurich automatically with inclination information derived from a Digital Elevation Model (DEM) and investigate the influence of inclination-enriched data on spatial accessibility. The spatial accessibility of three population groups (younger adults, older adults, and manual wheelchair users) to six main service providers (Healthcare Services, Daily Shopping, Public Services, Education, Leisure and Sports, Food and Drinks) was analysed using three different Floating Catchment Area (FCA) methods including 2SFCA, E2SFCA, and KD2SFCA. OSM footpaths were successfully enriched with inclination information using a high-resolution DEM. Results of the spatial accessibility analysis showed differences in the influence of accessibility enriched footpath data per population group, where manual wheelchair users were most affected in their spatial accessibility. Results from the 2SFCA method showed smallest areas that changed but a higher magnitude in change than the other two FCA methods, which yielded similar results. Furthermore, deprived areas concerning accessibility in the city of Zurich were found for all population groups and service providers in different areas of the city. The accessibility enriched footpath data can be used in spatial accessibility analysis, however, the data was not uploaded to OSM, as in other studies that applied an automated enrichment of OSM data. It can be concluded that mobility-impaired people such as manual wheelchair users are most affected by accessibility inhibiting barriers such as inclination. Furthermore, deprived areas concerning spatial accessibility are mainly found in areas where low accessibility and high demand and supply concur or when accessibility and supply are low. The results of this thesis confirmed the vulnerability of the mobility-impaired population in accessing public facilities, which strengthens the need for further research and development of an accessible city for all. Moreover, first insights in areas with lower spatial accessibility in the city of Zurich were made, which gives a basis for more in-depth research in this matter. The applied methods can be replicated if the necessary data is available

Review on smartphone sensing technology for structural health monitoring

Sensing is a critical and inevitable sector of structural health monitoring (SHM). Recently, smartphone sensing technology has become an emerging, affordable, and effective system for SHM and other engineering fields. This is because a modern smartphone is equipped with various built-in sensors and technologies, especially a triaxial accelerometer, gyroscope, global positioning system, high-resolution cameras, and wireless data communications under the internet-of-things paradigm, which are suitable for vibration- and vision-based SHM applications. This article presents a state-of-the-art review on recent research progress of smartphone-based SHM. Although there are some short reviews on this topic, the major contribution of this article is to exclusively present a compre- hensive survey of recent practices of smartphone sensors to health monitoring of civil structures from the per- spectives of measurement techniques, third-party apps developed in Android and iOS, and various application domains. Findings of this article provide thorough understanding of the main ideas and recent SHM studies on smartphone sensing technology

A Systematic Survey of ML Datasets for Prime CV Research Areas-Media and Metadata

The ever-growing capabilities of computers have enabled pursuing Computer Vision through Machine Learning (i.e., MLCV). ML tools require large amounts of information to learn from (ML datasets). These are costly to produce but have received reduced attention regarding standardization. This prevents the cooperative production and exploitation of these resources, impedes countless synergies, and hinders ML research. No global view exists of the MLCV dataset tissue. Acquiring it is fundamental to enable standardization. We provide an extensive survey of the evolution and current state of MLCV datasets (1994 to 2019) for a set of specific CV areas as well as a quantitative and qualitative analysis of the results. Data were gathered from online scientific databases (e.g., Google Scholar, CiteSeerX). We reveal the heterogeneous plethora that comprises the MLCV dataset tissue; their continuous growth in volume and complexity; the specificities of the evolution of their media and metadata components regarding a range of aspects; and that MLCV progress requires the construction of a global standardized (structuring, manipulating, and sharing) MLCV "library". Accordingly, we formulate a novel interpretation of this dataset collective as a global tissue of synthetic cognitive visual memories and define the immediately necessary steps to advance its standardization and integration

Spatial and Temporal Sentiment Analysis of Twitter data

The public have used Twitter world wide for expressing opinions. This study focuses on spatio-temporal variation of georeferenced Tweets’ sentiment polarity, with a view to understanding how opinions evolve on Twitter over space and time and across communities of users. More specifically, the question this study tested is whether sentiment polarity on Twitter exhibits specific time-location patterns. The aim of the study is to investigate the spatial and temporal distribution of georeferenced Twitter sentiment polarity within the area of 1 km buffer around the Curtin Bentley campus boundary in Perth, Western Australia. Tweets posted in campus were assigned into six spatial zones and four time zones. A sentiment analysis was then conducted for each zone using the sentiment analyser tool in the Starlight Visual Information System software. The Feature Manipulation Engine was employed to convert non-spatial files into spatial and temporal feature class. The spatial and temporal distribution of Twitter sentiment polarity patterns over space and time was mapped using Geographic Information Systems (GIS). Some interesting results were identified. For example, the highest percentage of positive Tweets occurred in the social science area, while science and engineering and dormitory areas had the highest percentage of negative postings. The number of negative Tweets increases in the library and science and engineering areas as the end of the semester approaches, reaching a peak around an exam period, while the percentage of negative Tweets drops at the end of the semester in the entertainment and sport and dormitory area. This study will provide some insights into understanding students and staff ’s sentiment variation on Twitter, which could be useful for university teaching and learning management

Rapid configurational analysis using OSM data: towards the use of Space Syntax to orient post-disaster decision making

This paper addresses the problem of the growing exposure of contemporary cities to natural hazards by discussing the theoretical, methodological and practical aspects of using the configurational approach as a framework to perform a variety of spatial analyses to better orient disaster management. It claims that enabling a quick assessment of the evolving spatial functioning of the urban grid would effectively contribute to support strategic decision-making and to make post-disaster planning decisions more explicit among stakeholders, thus boosting wider understanding and participation among the public. The paper starts with a brief review of some relevant work done by the research community to date, which highlights emergent opportunities for urban morphology studies and Space Syntax theory to trigger effective innovations in disaster management practice. Next, the paper proposes to adopt a fit-for-purpose analysis approach with the aim to achieve a higher procedural flexibility in the analysis workflow. This issue is treated with a special focus on the necessities of relief organisations which need to integrate and overlap numerous layers of information and consider the feasibility of the analysis by evaluating time and costs. The proposal considers the economy of the construction of the map to be fundamental for ensuring the feasibility of a quantitative spatial assessment in data scarce contexts such as cities affected by disasters. Moreover, it recognises that the unicity of the map is likely to enable a better communication among different stakeholders following a BIM-oriented model of cooperation, while allowing a faster response in multi-hazards scenarios. Consequently, the proposal challenges the idea of the existence of a uniquely correct way to translate reality into a model, but rather suggests using a set of simplification techniques, such as filtering, generalisation and re-modelling, on a single crowdsourced map of the urban street network to generate suitably customised graphs for subsequent analysis. This brings together two themes: the first concerns the modelling activity per se and how certain technicalities that seem minor facts can influence the final analysis output to a greater extent; the second regards the crowdsourcing of spatial data and the challenges that the use of collaborative datasets poses to the modelling tasks. In line with the most recent research trends, this paper suggests exploiting the readiness of the Open Street Map (OSM) geo-dataset and the improving computational capacities of open GIS tools such as QGIS, which has recently achieved a wider acceptance worldwide. To further speed up the analysis and increase the likeness of the configurational analysis method to be successfully deployed by a larger pool of professionals it also proposes to make use of a state-of-the-art Python library named OSMnx. In the end, the consequences of using Volunteered Geographic Information (VGI), open source GIS platforms and Python scripting to perform the analysis are illustrated in a set of suitable case studies

Enhancing Energy Efficiency in Connected Vehicles Via Access to Traffic Signal Information

This dissertation expounds on algorithms that can deterministically or proba-bilistically predict the future Signal Phase and Timing (SPAT) of a traffic signal by relying on real-time information from numerous vehicles and traffic infrastructure, historical data, and the computational power of a back-end computing cluster. When made available on an open server, predictive information about traffic signals’ states can be extremely valuable in enabling new fuel efficiency and safety functionalities in connected vehicles: Predictive Cruise Control (PCC) can use the predicted timing plan to calculate globally optimal velocity trajectories that reduce idling time at red signals and therefore improve fuel efficiency and reduce emissions. Advanced engine management strategies can shut down the engine in anticipation of a long idling interval at red. Intersection collision avoidance is another functionality that can benefit from the prediction. We start by exploring a globally optimal velocity planning algorithm through the use of Dynamic Programming (DP), and provide to it three levels of traffic signal information - none, real-time only, and full-future information. The no-information case represents the average driver today, and is expected to provide an energy efficiency minimum or baseline. The full-information case represents a driver with full and exact knowledge of the future red and green times of all the traffic signals along their route, and is expected to provide an energy efficiency maximum. We propose a probabilistic method that seeks to optimize fuel efficiency when only real-time only information is available with the goal of obtaining fuel efficiency as close to the full-future knowledge example as possible. We used Monte-Carlo simulations to evaluate whether the fuel efficiency gains found were merely the result of lucky case studies or whether they were statistically significant; we found in related case studies that up to 16% gains in fuel economy were possible. While these results were promising, the delivery of relevant and accurate future traffic signal phase and timing information remained an unsolved problem. The next step we took was towards building The next step we took was towards building traffic signal prediction models. We took several prescient techniques from the data mining and machine learning fields, and adapted them to our purposes in the exploration of massive amounts of data recorded from traffic Management Centers (TMCs). This manuscript evaluates Transition Probability Modeling, Decision Tree, Multi-Linear Regression, and Neural Network machine learning methods for use in the prediction of traffic Signal Phase and Timing (SPaT) information. signal prediction models. We took several prescient techniques from the data mining and machine learning fields, and adapted them to our purposes in the exploration of massive amounts of data recorded from traffic Management Centers (TMCs). This manuscript evaluates Transition Probability Modeling, Decision Tree, Multi-Linear Regression, and Neural Network machine learning methods for use in the prediction of traffic Signal Phase and Timing (SPaT) information. Finally, we evaluated the influence of providing SPaT data to vehicles. To that end, we investigated both smartphone and in-vehicle proof-of-concepts. An in-vehicle velocity recommendation application has been tested in two cities: San Jose, California and San Francisco, California. The two test locations used two different data sources: data directly from a TMC, and data crowdsourced from public transit bus routes, respectively. A total of 14 test drivers were used to evaluate the effectiveness of the algorithm. In San Jose, the algorithm was found to produce a 8.4% improvement in fuel economy. In San Francisco, traffic conditions were not conducive to testing as the driver was unable to significantly vary his speed to follow the recommendation algorithm, and a negligible difference in fuel economy was observed. However, it did provide an opportunity to evaluate the quality of data coming from the crowdsourced data algorithms. Predicted phase timing com-pared to camera-recorded ground truth data indicated an RMS difference (error) in prediction of approximately 4.1 seconds