The increased frequency of extreme events facing society is placing mounting pressure on cities and regions that need more robust resilience planning against growing uncertainty. Data augmented participatory methods, such as geodesign, offer much promise in supporting strategic planning to make our cities and regions more resilient. In that context, this study aims to contribute to a deeper understanding of geodesign practices in resilience planning, through a systematic review of the selected 487 studies available from various bibliographic databases. The results indicate that a total of 75 studies were connected to resilience thinking, with a focus on climate change, floods, and sea level rise risks. A significant cluster of those resilience-related studies worked, especially, on improving sustainability. A detailed analysis of 59 relevant geodesign case studies revealed a strong underlying emphasis on disaster risk reduction and management activities. This study also noticed two prominent approaches among the analysed case studies to future city scenario planning: computational (41 studies), and collaborative (18 studies). It is recommended that an explicit integration of these two approaches into the geodesign approach can assist future city resilience planning endeavours. Thus, future research should further investigate the utility of integrating data-driven modelling and simulation within a collaborative scenario planning process, the usability of digital tools such as planning support systems within a collaborative geodesign framework, and the value of the plan’s performance evaluation during resilience decision-making. Another area for future work is increased community engagement in city resilience practices. The geodesign approach can provide a comprehensive framework for bringing communities, decision-makers, experts, and technologists together to help plan for more resilient city futures. Finally, while geodesign’s explicit role in empirical resilience implementations has been found to be low in this systematic review study, there are significant opportunities to support evidence-based and collaborative city resilience planning and decision-making activities.

Mobile sensing uses features of mobile technology to monitor the environment and if a community participates in such monitoring, there can be community improvements in the quality of life. Despite the numerous potential benefits, the rate of citizen engagement in mobile sensing is generally low. Using the urban sensing project called “2Loud?,” this study was designed to uncover the reasons for non-participation and to discuss the motivations for intention to participate. A survey was administered to citizens who had been invited to participate but had decided not to participate. Examining the four motivational factors of “Concern,” “Belief,” “Technology,” and “Time” through a series of Kruskal-Wallis H Tests indicated that respondents had a statistically significant favorable attitude toward “Intention to Participate” if they had some concern and belief, and if they had access to technology and available time, compared to those who did not. However, binary logistic regression analysis indicated that only “Time” and “Technology” were significant predictors of “Intention to Participate.” These findings are discussed in light of social psychology and information technology theories, and recommendations for improved communication and engagement strategies are suggested.

Crowdsourced data hold great potential in creating metrics which can support city planning. Devices such as smartphones, with their sensor capabilities including GPS can be used to capture a wealth of mobility data at the scale of individual trips. However, the use of crowdsourced data for city planning is still hindered by doubts about their accuracy, objectivity and representativeness. This study proposes a validation process with five criteria – geographic coverage, origin-destination match, demographic match, distance–duration distributions, and route match – to assess different representativeness aspects of mobility data. The validation process is demonstrated using a crowdsourced data on bicycling in Sydney, Australia, compared with Census Journey to Work data. Results indicate a good overall fit of the sample against the population, but variations across the five criteria. Implications of these variations on the suitable uses for the crowdsourced data are discussed, and current limitations of the proposed approach are identified.

In an era of smart cities, artificial intelligence and machine learning, data is purported to be the ‘new oil’, fuelling increasingly complex analytics and assisting us to craft and invent future cities. This paper outlines the role of what we know today as big data in understanding the city and includes a summary of its evolution. Through a critical reflective case study approach, the research examines the application of urban transport big data for informing planning of the city of Sydney. Specifically, transport smart card data, with its diverse constraints, was used to understand mobility patterns through the lens of the 30 min city concept. The paper concludes by offering reflections on the opportunities and challenges of big data and the promise it holds in supporting data-driven approaches to planning future cities.

To realize the full potential of crowdsourced data collected by smartphone applications in urban research and planning, there is a need for parsimonious, reliable, computationally and temporally efficient data processing routines. The literature indicates that the opportunities brought by crowdsourced data in generating low-cost, bottom-up, and fine spatial and temporal scale data, are also accompanied by issues related to data quality, bias, privacy concerns and low accessibility. Using an exemplar case of RiderLog, a crowdsourced GPS tracked bicycling data, this paper describes and critiques the processes developed to transform this urban big data. Furthermore, the paper outlines the important tasks of formatting, cleaning, validating, anonymizing and publishing this data for the capital cities of each state and territory in Australia. More broadly, this research contributes to the foundational underpinnings of how to process and make available crowdsourced data for research and real world urban planning purposes.

In an era of data-driven smart cities, the possibility of using crowdsourced big data to support evidence-based planning and decision-making remains a challenge. Along with the increased availability and potential utility of crowdsourced data, there is a clear need to assess the validity of these data in order to determine their appropriate use for planning and management. Moreover, with growth and rapid urbanization in many cities, there are increasing challenges associated with urban mobility. The goal of this research is to develop an understanding of the geographical representativeness of crowdsourced data in the context of urban mobility through investigation of bicycling in Australian cities. In order to leverage both the geographic distribution and high volume of crowdsourced data for validity assessment, we present a two-stage statistical approach. First, we evaluate flow data through correlation between spatial interaction matrices in the presence of spatial autocorrelation. The second stage evaluates the quantity of information available within the interaction matrices. The approach is demonstrated with crowdsourced bicycling commuting routes recorded by the RiderLog app from 2010 to 2014 that are then correlated with census bicycling journey to work data. Data are from four of Australia’s state capital cities: Adelaide, Brisbane, Melbourne and Perth. These methods assess the representativeness of individual bicycle routes that address the full pattern of flows within multiorigin multidestination systems and incorporate spatial autocorrelation. Results indicate that these crowdsourced data are geographically representative of regional travel where there are higher data volumes, generally in central business districts and occasionally in outlying areas. This research provides insights into both methods for statistical comparison of flow data and the use of crowdsourced bicycling routes for urban planning and management.