March 2019 Summaries

This is the latest version of CARTO VL, which introduces a new feature called cluster labels, allowing users to label spatial clusters in their datasets. The addition also exposes feature aggregation values per cluster in the API, enabling users to access this information through variables. Another new expression, GlobalHistogram, provides global-based calculations for drawing histogram charts, maintaining consistency with other viewport and global expressions. Additionally, more examples have been added to the VL Developer Center, including interactive filter examples, dynamic styling using variables, and enhanced animation capabilities.

In the latest release of CARTO Reveal, a location intelligence solution for retailers and real estate firms, the platform has been improved to provide faster and higher quality insights, addressing the needs of various stakeholders including expansion managers, data science teams, and executive decision makers. The solution now incorporates new features such as CARTO VL and Airship, allowing organizations to leverage external data streams like human mobility and transaction data, and integrate internal data on store performance. This update aims to support the growing demand for spatially savvy businesses amidst a shifting retail landscape.

This Map of Travel in Greater Santiago, Chile is a unique data visualization created by Ariel López, an urban planner and transportation engineer, that uses origin-destination matrices to map out over 18 million trips taken during a normal work day. The map provides insights into human factors behind transit, such as the impact of gender on transport choice, and the distribution of private vehicle usage along major transit corridors. It also allows users to explore non-motorized trips and time-of-day patterns, revealing that walking trips peak around mid-day and at 6 pm in Downtown Santiago. The map is an example of how Location Intelligence can be used to create a more holistic approach to urban mobility problems, combining scientific standards with the human element. CARTO's interactive widgets enable users to filter and drill into data, providing new insights into transit systems in modern cities.

CARTO has released a 125M polygon dataset that increases the coverage of building footprints in OpenStreetMap and CARTO basemaps, which can be used to create printable maps. The dataset can be ingested into CARTO using the COPY API, which leverages streaming technologies such as generators in Python. Once ingested, users can visualize and style the data using the CARTO Maps API, and then export the map for printing using the static maps API. The carto-print library provides a simple way to compose images of different sizes and DPI, allowing users to create high-quality prints from their maps. Additionally, the library can be used to export images of BUILDER maps in any size and DPI, making it easy to create posters or other printed materials.

The Atlas of Inequality is a project launched by MIT Media Lab's Human Dynamics Group and Universidad Carlos III de Madrid that explores behavioral patterns and investigates segregation, an ongoing issue exacerbated by personal choices. The team behind the Atlas aims to visualize and inform viewers about places where they are actively segregated, with the goal of promoting greater diversity in daily interactions. The Atlas uses aggregated location data from Cuebiq to examine human mobility patterns and create a diversity index, visualizing segregation as a function of income levels. The project provides an interactive map that allows users to filter by location categories and view information on resources and methodology. The creators hope that the Atlas can inspire small behavioral changes that can lead to greater outcomes for all, particularly in reducing segregation in daily lives.