September 2021 Summaries

CARTO has been selected to continue in Phase 1 of the AI4Cities project, Solution Design Phase, and is now further selected for Phase 2, where it will work on a prototype of its proposed mobility solution, FutureUrbanMobility. This solution aims to optimize shared mobility modes and active mobility into urban planning, using Location Intelligence and Machine Learning. CARTO's selection in the AI4Cities project is part of the EU-funded Pre-Commercial Procurement (PCP) project, which seeks to accelerate carbon neutrality through AI solutions. The project's focus on addressing traffic congestion and inefficient transport systems in European cities, accounting for 24% of GHG emissions, aligns with CARTO's goals in developing its FutureUrbanMobility solution. With the support of AI4Cities partners, including City of Helsinki, Paris Region, Amsterdam, Copenhagen, Tallinn, and Stavanger, CARTO will work to develop a prototype that simulates optimal locations for micro-mobility services, supports decision-making based on predictive models, and enhances user experience through gamification techniques.

The increasing frequency of extreme weather events such as hurricanes, wildfires, and floods is linked to climate change, with average temperatures having risen by 1.2C since the industrial era. Spatial storytelling and environmental analysis are crucial in protecting our world against climate change, and maps have been used by media organizations and public and private organizations to build awareness of recent events and mitigate devastating effects. Maps created using data from various sources such as the National Weather Service, National Hurricane Center, and California Public Utilities Commission have helped authorities make decisive emergency decision-making, identify at-risk neighborhoods, and deliver life-saving resources before and after disasters struck. Wildfires in California are growing more dangerous due to accumulation of wood fuel in forests, higher population, and greater electricity transmission lines, and maps have been used to visualize active fire perimeters, density of smoke levels, and property risks to local agricultural businesses. The use of maps has also helped disseminate vital information to residents, rescue agencies, and relief organizations, raising awareness of long-term health hazards exposure to high levels of smoke.

Vue.js is a versatile and reactive framework suitable for building web applications, including spatial applications that utilize CARTO's features. CARTO provides essential backend functionality for spatial apps, allowing developers to focus on frontend user experience. The platform integrates seamlessly with Vue.js, and deck.gl is recommended for visualization capabilities. An example application demonstrates best practices for building scalable spatial apps with Vue.js and CARTO. The example uses well-supported open-source libraries like Vue Material, Vuex, Vue Router, Apache ECharts, and Turf.js to manage state, navigation, and spatial analysis. A layerManager module handles layers, and state management is achieved using Vuex. To get started, developers should read the guide and contact the platform if needed.

The global supply chain crisis is being exacerbated by factors such as pandemic demand disruption, driver shortages, post-Brexit checks, and fuel shortages, resulting in empty shelves and disrupted retailer operations. To mitigate this crisis, Forbes suggests that supply chain workers should prioritize anti-fragility, sustainability, access, and equity over productivity and profit goals. Spatial Data Science can help identify CPG demand hotspots, optimize Modern Distribution Management, Fleet Management, and Route Optimization, reducing costs and improving visibility. Accurate demand modeling using spatial models can predict and test future demand scenarios, while routing engines use heuristics to find the shortest and most efficient paths for deliveries. The gig economy has also impacted last-mile logistics, enabling delivery drivers to quickly download apps and scan multiple items in one go. By leveraging Spatial Data Science, supply chain firms can optimize pick-up and drop-off site selection, predict demand using new data streams, and visualize existing fleets to improve efficiency and reduce costs.

The Cumbre Vieja volcano on La Palma in the Spanish Canary Islands erupted, causing destruction of 300 homes and forcing the evacuation of over 6,000 residents, with no casualties reported so far. A team has been mapping the lava flow using data from various sources, including the Copernicus Emergency Management Service and Google's Open Buildings dataset, to estimate its advance to the sea and potential damage to property. The maps and models created by this effort aim to visualize the situation and provide insights for catastrophe modeling and economic impact estimation, offering solidarity with the affected residents and support during their difficult period.

The Spatial Data Science Conference '21 will be held as a virtual conference from October 25th to 28th, featuring over 30 sessions across four days. The event aims to bring together spatial data science experts and leaders for networking opportunities and knowledge sharing. Notable speakers include Anita Graser, Martin Fleischmann, Claire Byrne, Jonathan Americo, Giulia Carella, Pelayo Arbués, Taylor Reich, Andrea Gorrini, Iacopo Testi, Geoff Michener, and others from various organizations such as TomTom, CARTO, the University of Liverpool, and Google Cloud. The conference also features sponsors like Fulcrum, Unacast, Veraset, Safegraph, OmniSci, SingleStore, dataPlor, and Foursquare. Registration is now open for free, making it possible to join the largest virtual gathering of spatial data science experts worldwide.

Onshore wind energy is rapidly growing globally, with its capacity projected to reach nearly 750 GW by next year. To achieve maximum potential and profitability, developers need to accurately site tens of thousands of turbines. The National Renewable Energy Laboratory (NREL) has been researching ways to advance renewable energy through spatial analysis and visualization. NREL's research team used spatial data science to analyze siting challenges for wind technology, exploring scenarios with varying levels of restrictions on turbine placement. Their findings suggest that siting restrictions can significantly impact future wind capacity, particularly in regions with high demand for clean energy. The team also visualized their results using interactive maps and supply curves to provide a comprehensive understanding of the potential for wind energy development.

The COVID-19 pandemic has had a profound impact on the world, with widespread devastation and disruption caused by the virus. In response to this crisis, organizations have come together to provide critical support and resources to communities around the globe. A wide range of mapping tools and platforms, including CARTO, have been utilized to track the spread of the virus, analyze vaccination progress, and provide insights into community needs and vulnerabilities. These efforts have highlighted the importance of effective communication, community support, and targeted interventions in slowing the spread of the pandemic and mitigating its impact on vulnerable populations. By leveraging location-based data and analytics, organizations can better understand the complexities of the crisis and develop more effective strategies for responding to it.

The analysis of site selection strategies for large restaurant brands in the US reveals that proximity to other POIs, such as restaurants and shopping centers, plays a significant role in determining their locations. The study used data from Safegraph's Core Places dataset and found that similar brands tend to cluster together, with first-order neighbors often being from the same sub-category or top-category. Modeling the density of selected POIs showed that distance-based attributes significantly improved predictive accuracy compared to models only including urbanity and business density. The analysis also revealed that the density of selected POIs decreases with distance to closest POI, but not as much for POIs in the same category.