No Arabic abstract
As buildings are central to the social and environmental sustainability of human settlements, high-quality geospatial data are necessary to support their management and planning. Authorities around the world are increasingly collecting and releasing such data openly, but these are mostly disconnected initiatives, making it challenging for users to fully leverage their potential for urban sustainability. We conduct a global study of 2D geospatial data on buildings that are released by governments for free access, ranging from individual cities to whole countries. We identify and benchmark more than 140 releases from 28 countries containing above 100 million buildings, based on five dimensions: accessibility, richness, data quality, harmonisation, and relationships with other actors. We find that much building data released by governments is valuable for spatial analyses, but there are large disparities among them and not all instances are of high quality, harmonised, and rich in descriptive information. Our study also compares authoritative data to OpenStreetMap, a crowdsourced counterpart, suggesting a mutually beneficial and complementary relationship.
Getting people cycling is an increasingly common objective in transport planning institutions worldwide. A growing evidence base indicates that high quality infrastructure can boost local cycling rates. Yet for infrastructure and other cycling measures to be effective, it is important to intervene in the right places, such as along desire lines of high latent demand. This creates the need for tools and methods to help answer the question where to build?. Following a brief review of the policy and research context related to this question, this paper describes the design, features and potential applications of such a tool. The Propensity to Cycle Tool (PCT) is an online, interactive planning support system which was initially developed to explore and map cycling potential across England (see www.pct.bike). Based on origin-destination data, it models and visualises cycling levels at area, desire line, route and route network levels, for current levels of cycling, and for scenario-based cycling futures. Four scenarios are presented, including Go Dutch and Ebikes, which explore what would happen if English people cycled as much as Dutch people and the potential impact of electric cycles on cycling uptake. The cost effectiveness of investment depends not only on the number of additional trips cycled, but on wider impacts such as health and carbon benefits. The PCT reports these at area, desire line, and route level for each scenario. The PCT is open source, facilitating the creation of additional scenarios and its deployment in new contexts. We conclude that the PCT illustrates the potential of online tools to inform transport decisions and raises the wider issue of how models should be used in transport planning.
E-Scooters are changing transportation habits. In an attempt to oversee scooter usage, the Los Angeles Department of Transportation has put forth a specification that requests detailed data on scooter usage from scooter companies. In this work, we first argue that L.A.s data request for using a new specification is not warranted as proposed use cases can be met by already existing specifications. Second, we show that even the existing specification, that requires companies to publish real-time data of parked scooters, puts the privacy of individuals using the scooters at risk. We then propose an algorithm that enables formal privacy and utility guarantees when publishing parked scooters data, allowing city authorities to meet their use cases while preserving riders privacy.
As the underground infrastructure systems of cities age, maintenance and repair become an increasing concern. Cities face difficulties in planning maintenance, predicting and responding to infrastructure related issues, and in realizing their vision to be a smart city due to their incomplete understanding of the existing state of the infrastructure. Only few cities have accurate and complete digital information on their underground infrastructure (e.g., electricity, water, natural gas) systems, which poses problems to those planning and performing construction projects. To address these issues, we introduce GUIDES as a new data conversion and management framework for urban underground infrastructure systems that enable city administrators, workers, and contractors along with the general public and other users to query digitized and integrated data to make smarter decisions. This demo paper presents the GUIDES architecture and describes two of its central components: (i) mapping of underground infrastructure systems, and (ii) integration of heterogeneous geospatial data.
Building Management Systems (BMS) are crucial in the drive towards smart sustainable cities. This is due to the fact that they have been effective in significantly reducing the energy consumption of buildings. A typical BMS is composed of smart devices that communicate with one another in order to achieve their purpose. However, the heterogeneity of these devices and their associated meta-data impede the deployment of solutions that depend on the interactions among these devices. Nonetheless, automatically inferring the semantics of these devices using data-driven methods provides an ideal solution to the problems brought about by this heterogeneity. In this paper, we undertake a multi-dimensional study to address the problem of inferring the semantics of IoT devices using machine learning models. Using two datasets with over 67 million data points collected from IoT devices, we developed discriminative models that produced competitive results. Particularly, our study highlights the potential of Image Encoded Time Series (IETS) as a robust alternative to statistical feature-based inference methods. Leveraging just a fraction of the data required by feature-based methods, our evaluations show that this encoding competes with and even outperforms traditional methods in many cases.
Developing nations are particularly susceptible to the adverse effects of global warming. By 2040, 14 percent of global emissions will come from data centers. This paper presents early findings in the use AI and digital twins to model and optimize data center operations.