Monitoring movement in the smart city : opportunities and challenges of measuring urban bustle

One of the promises of the smart city concept is using real-time data to enhance policy making. In practice, such promises can turn out to be either very limited in what is actually possible or quickly trigger dystopian scenarios of tracking and monitoring. Today, many cities around the world already measure forms of urban bustle, i.e. how busy it is during specific periods of time. They do this for all kinds of purposes like optimising mobility flows, attracting tourism, monitoring safety during events or stimulating the local economy, and they employ divergent technologies: from analogue counting, over surveys, to more advanced near real-time tracking using mobile operator data. This fragmentation of approaches to measuring urban bustle creates some challenges for cities related to privacy, vendor lock-in, comparability of data, data quality and accuracy, historical and predictive analysis of data and so on. To tackle these challenges and formulate a standardised approach to measuring urban bustle, the thirteen largest cities in Flanders (Belgium), together with local technology vendors, co-created a “definition manual”; a document outlining indicators and relevant technologies for measuring urban bustle, as well as shared profile descriptions of residents and visitors of the city. This paper outlines the process and presents the results, an agreed-upon framework of standard profiles and indicators, which are useful to academics, public servants and technology companies involved in this topic

A study of real-time open data publication strategies

Cities are increasingly investing in sensors to attain data-driven policies, yet there is no clear strategy how this public sector information should be published as live Open Data. Studies suggest to keep the data moving by using a publish/subscribe model instead of traditional HTTP polling, however, more research is required to know whether this should become the standard method for every use case. Therefore, we are categorizing streams of sensor observations based on the (1) minimum time between two observations, (2) acceptable end-user latency, (3) expected network latency and (4) amount of end-users. A benchmark of streams with different characteristics gives Open Data publishers better insights which strategy to choose. An action plan will be created and disseminated with the 13 biggest cities of Flanders through the Smart Flanders program

Linked data event streams as an open data interface for live data consumption in integrated services

Het integreren van datasets is een van de uitdagingen bij het bouwen van toepassingen. Zo moet een weer-applicatie naast voorspellingen ook waarnemingen van weerstations ophalen. Het doel van geïntegreerde diensten is om toepassingen een meer samenhangend en uniform toegangspunt tot een groot aantal datasets te bieden. Toegang tot de historische en actuele veranderingen van een dataset wordt momenteel echter opgelost via dataset-exports en zoek-interfaces, die respectievelijk niet schaalbaar zijn voor snel veranderende datasets en onvoldoende flexibiliteit bieden om elke soort vraag op te lossen. Linked Data Event Streams (LDES) lost dit probleem op door een dataset voor te stellen als een groeiende verzameling onveranderlijke objecten wat vergelijkbaar is met een newsfeed: telkens wanneer een dataset verandert, wordt een update aan de verzameling toegevoegd. De resultaten van dit proefschrift behandelen verschillende aspecten van het gebruik van LDES om datasets te publiceren als Open Data: richtlijnen voor het publiceren van live datasets, anonimiseren van Automatic Number Plate Recognition (ANPR) datasets, het vormgeven van data conform specificaties, het fragmenteren van cultureel erfgoed collectiebeheersystemen en Next Generation Service Interfaces for Linked Data (NGSI-LD) context brokers, en het mogelijk maken van geïntegreerde diensten met LDES als bron van waarheid

Using ANPR data to create an anonymized linked open dataset on urban bustle

ANPR cameras allow the automatic detection of vehicle license plates and are increasingly used for law enforcement. However, also statistical data generated by ANPR cameras are a potential source of urban insights. In order for this data to reach its full potential for policy-making, we research how this data can be shared in digital twins, with researchers, for a diverse set of machine learning models, and even Open Data portals. This article's key objective is to find a way to anonymize and aggregate ANPR data in a way that it still can provide useful visualizations for local decision making. We introduce an approach to aggregate the data with geotemporal binning and publish it by combining nine existing data specifications. We implemented the approach for the city of Kortrijk (Belgium) with 43 ANPR cameras, developed the ANPR Metrics tool to generate the statistical data and dashboards on top of the data, and tested whether mobility experts from the city could deduct valuable insights. We present a couple of insights that were found as a result, as a proof that anonymized ANPR data complements their currently used traffic analysis tools, providing a valuable source for data-driven policy-making

A sustainable method for publishing interoperable open data on the web

Smart cities need (sensor) data for better decision-making. However, while there are vast amounts of data available about and from cities, an intermediary is needed that connects and interprets (sensor) data on a Web-scale. Today, governments in Europe are struggling to publish open data in a sustainable, predictable and cost-effective way. Our research question considers what methods for publishing Linked Open Data time series, in particular air quality data, are suitable in a sustainable and cost-effective way. Furthermore, we demonstrate the cross-domain applicability of our data publishing approach through a different use case on railway infrastructure-Linked Open Data. Based on scenarios co-created with various governmental stakeholders, we researched methods to promote data interoperability, scalability and flexibility. The results show that applying a Linked Data Fragments-based approach on public endpoints for air quality and railway infrastructure data, lowers the cost of publishing and increases availability due to better Web caching strategies

Publishing base registries as linked data event streams

Fostering interoperability, Public Sector Bodies (PSBs) maintain datasets that should become queryable as an integrated Knowledge Graph (KG). While some PSBs allow to query a part of the KG on their servers, others favor publishing data dumps allowing the querying to happen on third party servers. As the budget of a PSB to publish their dataset on the Web is finite, PSBs need guidance on what interface to offer first. A core API can be designed that covers the core tasks of Base Registries, which is a well-defined term in Flanders for the management of authoritative datasets. This core API should be the basis on which an ecosystem of data services can be built. In this paper, we introduce the concept of a Linked Data Event Stream (LDES) for datasets like air quality sensors and observations or a registry of officially registered addresses. We show that extra ecosystem requirements can be built on top of the LDES using a generic fragmenter. By using hypermedia for describing the LDES as well as the derived datasets, agents can dynamically discover their best way through the KG, and server administrators can dynamically add or remove functionality based on costs and needs. This way, we allow PSBs to prioritize API functionality based on three tiers: (i) the LDES, (ii) intermediary indexes and (iii) querying interfaces. While the ecosystem will never be feature-complete, based on the market needs, PSBs as well as market players can fill in gaps as requirements evolve