DataX Connect’s Post

“These data centers will not last. I think that’s another important point for people to realize. These data centers are ephemeral. They know that they will eventually have to disband. This is the kind of perversity of data centers coming into many communities with these promises of economic growth. There is certainly a lot of jobs that are created to construct the data center. But once a data center has actually been constructed, it’s only a handful of people who actually run a facility. So, in some cases, just a dozen people, or two dozen people, run a facility that is consuming as much electricity as a small city. A data center life is between five and twenty years. This is not a permanent industry. It is extractive, like mines.” Steven Gonzalez Monserrate – Thirsty Data: Data Centers increasing impact on fresh water https://lnkd.in/etf3H3Ub

📣 New paper 📣 We investigated the utility of five models that create synthetic urban mobility data from raw privacy-sensitive data. Tl;dr: synthetic trips do not provide the expected high flexibility and utility and should be used carefully. Helena Mihaljevic https://lnkd.in/dCMx6Ppi Why synthetic data? Human movement data is highly sensitive, however, data sharing is desirable for many use cases, including city planning or or demand-based transit. Synthetic data, in this context, is created through models that learn respective distributions from raw data and maintain these. The goal is to create high-utility privacy-friendly synthetic datasets. How is utility measured? Typically, such synthetic data models are evaluated by comparing distributions, e.g., the spatial distribution, between raw and synthetic data. The higher the similarity the higher the utility is considered. However, this approach has shortcomings: Distributions are typically discretized, e.g., a spatial distribution based on a grid. The resolution of such grids thereby highly influences the conclusion about the maintained utility: a high similarity on a 100m resolution has different implications than on a 1km resolution. Also: high similarity based on one distribution does not indicate a general high utility. For example, high similarity of spatial distributions does not allow conclusions about temporal distributions. Single distributions also do not reflect actual real-life use cases. What did we evaluate? We selected 4 tasks that closely reflect real-life tasks that trip data is used for to obtain a more realistic utility evaluation: trip lengths, traffic volume, road preference, and traffic flow at intersections. We evaluated the utility of five state-of-the-art models, AdaTrace, PrivTrace, DP-Loc, a BiLSTM-based model, and TrajGAIL, using the designated utility metrics on a dataset comprising approximately 30,000 bicycle trips in Berlin. First of all, none of the 5 evaluated models provide synthetic data on a level that is fine-granular enough to match the road network. Thus, we included a step of map matching. Then, we introduced routing-engine-generated trips (like GoogleMaps) as a baseline, as they provide a privacy-friendly way of fine-granular routes to connect a start and an endpoint. Our results Out of the 5 evaluated models, two fail to produce results suitable for our dataset and map-matching approach. The remaining 3 models somewhat maintain spatial distribution, one even with differential privacy guarantees. However, all models struggle to produce meaningful sequences of geo-locations with reasonable trip lengths and to model traffic flow at intersections accurately. It is worth noting that trip data encompasses various relevant characteristics beyond spatial distribution all of which are discarded by these models. Our results imply that current models fall short in their promise of high utility and flexibility.

Designing new #infrastructure means planning for uncertainty. Most assets will remain in place for a number of years so need to be fit for purpose throughout their long life-cycle 🌉 Alongside factors including the cost-benefit ratio, designers have to consider political turbulence, shifts in population, technological developments and climate change. Some of these can be challenging to model with accuracy 🌍 So how can the application of data science aid the process? Our Chief Data Scientist Daniel Scott discusses with the New Statesman how businesses can use data to make better decisions around infrastructure #planning and delivery 👇 https://lnkd.in/g6QAiBAG #Digital #Data #Innovation #Design

🤓 Sharing Knowledge: Smart Cities Conference 🤓 My colleague Jean-Paul Kasprzyk from the GeoScITY lab (University of Liège) will be at the Smart Data and Smart Cities (SDSC2024) at Athens, Greece next week. He will present a paper we co-authored with Roland Billen on an hybrid database solution for Digital Twins using both NoSQL and relational models. We develop ideas around a middleware that can be used to digest and serve data from heterogeneous sources according to your own needs. If you don't yet know the laboratory, it develops methods and tools for the management of Digital Twin urban: https://lnkd.in/eKntYSrq 📰 If you don't have the opportunity to follow the conference, stay tuned, we will publish the results later. #research #openaccess #DigitalTwins #DatabaseSolutions #NoSQL #SmartCities #SDSC2024

I recently undertook an illuminating project involving the creation of a service area network for schools within a specific district in Zimbabwe. 🏫🗺️ The goal was to ensure efficient access to education by analyzing the proximity of schools and their service areas. 🚌✨ While the process was insightful, it was not without its share of unexpected twists. 🔄 Some schools appeared to have no service areas, leaving an unsettling gap in the analysis. 🙅♂️ Additionally, there were instances where service areas were assigned distances that seemed oddly inflated, far from the reality on the ground. ---- See Map for Reference ---- 🛤️🚶Upon deeper investigation, it became clear that the root of these challenges lay in the data itself, particularly the road information sourced from OpenStreetMap (OSM). 🗺️ As we all know, OSM is a powerful platform driven by contributors from across the globe, each adding a piece to the global mapping puzzle. 🌐🧩In this instance, the accuracy and comprehensiveness of the data proved pivotal. Those schools with missing service areas were located in regions where road data was scant on OSM. Similarly, inaccuracies in distance measurement stemmed from incomplete or outdated road information. 🛣️This experience underscores a fundamental truth: >>Data is the backbone of effective geographic analysis. << 📊📍 The success of projects like these hinges on the quality and depth of the data pool. And this is where we, as a community, can make a real impact. 💪🤝 🌟 The Call to Action: 🌟 🔹 Map the Unmapped: Let's fill in the gaps together! By contributing road data, especially in underserved areas, we can ensure that critical analyses like these are robust and truly reflective of the ground reality. 🔹 Update and Validate: Regularly updating OSM information and validating its accuracy can make a world of difference. A minor edit from you might mean a more accurate service area for a school, a clinic, or a community center. 🔹 Empower the Local: Amplify local mapping efforts. No one knows a region better than those who live there. Empowering local mappers can bridge the data divide and lead to more contextually precise analyses. 💬 Let's Turn the Tide! 💬 As we navigate the world of GIS and geospatial analysis, let's remember that every contribution to platforms like OpenStreetMap is a step towards a more connected, informed, and equitable world. 🌐🌍 Your mapping matters. Your data makes a difference. Together, we can create maps that mirror reality and foster positive change. 🗺️💡 #OpenStreetMap #MappingMatters #Geospatial #DataAccuracy #CommunityMapping #GIS #DataForGood

Modern innovations mined from vast data repository. ‘Old’ data is providing key insights for a range of projects from construction and forestry to health, Glasgow cloud, data & AI conference heard ⬇️ https://lnkd.in/eTUTyR-9 #cloud #data #AI #Scotland

A fantastic article featuring WSP in the UKs Chief Data Scientist Daniel Scott discussing how Data Science can help to future proof infrastructure. "The unknowns we face today are arguably more pronounced than ever before ... AI can’t solve every issue affecting infrastructure but applied in the right way with the right leverage, it will have a massive impact,” #innovation #datascience #dataanalytics #thewspexperience #adaptiveplanning

🚨 Announcement 🚨 We are very excited to announce a new short course: Open Data Platforms and Licences for Cities 🏢 Convened by Dr Matthew N., Open Data Platforms and Licences for Cities short course is designed to offer students and professionals insights to understand urban data more comprehensively. This course lays the groundwork in open data and its licenses, educating participants on correct procedures for data sharing and licensing. It is also an introduction to urban data, covering aspects such as licensing, analysis, visualisation, citizen science and participatory planning. Each module is structured to focus on each of these aspects and includes a practical component to assist users in navigating and contributing innovative voluntary geographic information to the City Futures Research Centre’s Colouring Australia platform. (https://lnkd.in/gRxniWEB) What will I gain from this course? This course is to upskill participants to stay abreast of the evolving trends and dialogues in open data. The course is entirely online, offering flexibility allowing participants to learn at their own pace and schedule. The course content includes pre-recorded lectures, readings, interactive learning activities, and discussion forums. Upon course completion, participants will earn accreditation from the UNSW, one of Australia’s leading universities. Who is this course intended for? This course is aimed at researchers, students, and professionals working in city planning, policy, urban geography, land economics, real estate, property, and construction sectors. It is apt for anyone looking to enhance their knowledge and skills in open data related to urban development. It is also suitable for anyone aspiring to increase their proficiency and knowledge in open data pertaining to urban systems. Best part of this course? There is no fixed start or end date so you can complete it at your own pace! Register to secure your ticket here:  https://lnkd.in/ef6xGiyE

Useful reminder by Alberto Abella, PhD in the FIWARE track of the #DataSpacesSymposium: the Meloda 5 metric to assess open data reusability (https://lnkd.in/eebPUVSc, https://www.meloda.org/). Seems to address a large subset of the #FAIRData Principles, thus relevant for FAIR Data Spaces and our current related work in this direction. Marco H. Lina Molinas Comet Paul Moosmann Ioannis Chrysakis Johannes Theissen-Lipp

It is time to reshape the way we think about our communities. Our Architectural Data Consultancy service brings you closer to the heart of communities with neighbourhood analytics for smoother project approvals and enhanced local support #yemetech #sfs #cdp #communities #architecture #spatialdata #analytics #gis #mapping #neighbourhoods #fulfilment #communityplanning #sustainabledesign