May 2020 Summaries

Cities around the world are transforming their urban areas after the COVID-19 pandemic, with a focus on creating safe social distancing measures. These transformations include expanding pedestrian zones and making roads more pedestrian-friendly, which may lead to an increase in community belonging. Human mobility data is being leveraged to identify areas where pedestrians accumulate and understand journey patterns. The number of bike users has also increased, with cities using Location Intelligence to visualize cycling routes and optimize infrastructure. Additionally, the micromobility industry, including scooters and electric cars, is growing exponentially, and companies are analyzing location data to optimize their services. Finally, public transportation systems are being optimized to ensure continued movement of citizens while maintaining social distancing guidelines. Spatial Data Science and Location Intelligence play a key role in ensuring citizen safety and enabling sustainable mobility strategies.

The study employs a novel empirical approach that synergises recent advancements in Machine Learning, new data sets, and Spatial Data Science techniques to systematically examine gentrification trends in London. The research team used Principal Component Analysis (PCA), K-Means clustering, and in-depth spatial analysis to identify neighborhoods in London that have undergone recent gentrification, explore which neighborhoods are likely to be next in line, and present data code and novel interactive visualisations as a comprehensive tool for supporting policy and decision making in the city. The study found that gentrifying LSOAs were predominantly located in traditionally richer upscale West London boroughs and East London, affecting over half-a-million residents, with super-gentrification, marginal gentrification, and mainstream gentrification simultaneously taking place in London and unlikely to diminish in the near future.

The World Health Organization declared the COVID-19 pandemic a worldwide pandemic over two months ago, leading governments to implement unprecedented measures to control its spread. While these measures have been effective in reducing the virus's reproduction ratio, they have also caused significant economic and social collateral damage. As governments begin to lift lockdowns and transition into a new normal, location intelligence will play a critical role in helping businesses and administrations make informed decisions about adapting to the new status quo and optimizing resources for recovery. New public datasets are now available in the Data Observatory, including those on infection rates, risk factors, social media sentiment, human mobility, and economic indicators. These datasets can be used to analyze the socio-demographic and socio-economic impact of the pandemic, track changes in behavior and epidemiological indicators, and inform policy decisions at local and national levels.

GIS Indoor Mapping: Workplace Social Distancing` Many large companies are working out their return to offices, factories, restaurants, and commercial spaces as the world slowly emerges from lockdown. Location analytics plays a crucial role in social distancing strategies, with 2-meter distances being key. Companies like Perkins and Will use GIS indoor mapping software, such as CARTO, to plan staggered returns, monitor occupancy, and ensure safety. This can include tracking floor occupancy, identifying off-limits workstations, and visualizing conference room occupancy. The solution also enables the integration of apps for contact tracing and allows employees to share health information. Similarly, other industries like manufacturing, events, and hospitality are turning to indoor solutions to understand people flow and optimize routes. GIS indoor mapping can support return to work programs by providing digital contact tracing visualization, ensuring efficiency, and promoting employee safety.

HIPAA Compliance in Geospatial Healthcare Analytics` involves processes for anonymization and aggregation, as Spatial Data Science best practices. The US Federal Government's Health Insurance Portability and Accountability Act (HIPAA) directly addresses data security and protection of Personally Identifiable Information in the healthcare industry. HIPAA compliance is crucial when working with health information in spatial analysis, particularly with geocoding, where there are two schools of thought on deidentification. CARTO ensures HIPAA compliance by only sending address string columns to its geocoding provider, which meets the requirements set forth in the Privacy Rule. The company also ensures strict GDPR compliance for customers within the European Union. Key use cases include Social Determinant of Health Analysis, Healthcare Access Analysis, and Medical Site Selection, where spatial analysis provides a more detailed picture of healthcare access and informs optimal resource allocation. CARTO advises on maintaining HIPAA compliance for its clients in the healthcare space, providing solutions that fit their organization's needs.

New York City has over 6,000 high-rise buildings, with 274 skyscrapers standing over 150m tall, whereas London only has 33, leading one to assume that New York has the higher average building height. However, when zooming out to include city limits, both cities have a similar figure at around 8m. This suggests that neither city actually has a significantly higher average building height than the other. The difference in building heights can be attributed to various factors such as the number of low buildings and garages in London's dataset compared to New York City's more detailed data. By combining building height analysis with local population densities, researchers can better understand the actual lived-in density of cities like New York and London. This type of spatial analysis is crucial for research teams in Real Estate, Commercial Market Analysis, Trade Area Analysis, and Site Selection, as it provides valuable insights that improve investment strategies and justifies pricing for sales and rentals.

The COVID-19 pandemic has had a significant impact on retail sales, with an 8.7% decline in US retail sales compared to the pre-pandemic period. To better understand the commercial and economic impact of the virus across different industries and geographies, retailers are turning to new data streams such as human mobility, socio-economic, and financial insights. A Retail Impact Scoreboard developed by Unacast tracks changes in visitation trends across various industries and regions, providing valuable insights for recovery planning. Consumer confidence is a key indicator of activity and recovery, with recent surveys indicating low levels of confidence among Americans. By analyzing data from Mastercard transactions during the pandemic, retailers can gain insights into customer behavior and spending patterns, which can inform recovery strategies. The use of spatial data and real-time human behavior analysis can help identify early signals of potential recovery in different regions, such as Georgia, where a trend analysis revealed mixed short-term trends with no clear signal visible before reopening, but upwards momentum observed after reopening.

The author of the text is playing with the new Google BigQuery connector for their mapping platform, CARTO, which allows seamless import of OSM features into their maps using pure SQL and no local downloading. They started by querying the OpenStreetMap public dataset in BigQuery, then saved the query results as a personal dataset in their account, and finally queried this dataset from CARTO's BigQuery connector to import 10,000 Dutch bridges onto their map with remarkable speed. The author is excited about the potential of this feature and invites readers to get started with the BigQuery Connector beta version available for CARTO Enterprise users.