January 2020 Summaries

Spatial Data Science is a rapidly growing field that continues to expand its reach as industries recognize its value, with more enterprises maintaining Data Science and GIS departments, but still facing challenges in collaboration between these teams. The preferred language for Spatial Data Science operations is a topic of debate, with Python edges out R, but both have their advantages. The field faces a talent gap due to the lack of resources and qualified candidates, but academic and professional training programs are emerging to address this issue.

The utilities industry is facing significant challenges due to rising customer churn and increasing competition, with nearly 15% of domestic customers switching suppliers in the US and EU every year. Acquiring new customers can be 5-25 times more expensive than retaining existing ones, making it crucial for companies to focus on improving customer retention strategies. Spatial Data Science techniques are being used to capture spatial patterns that standard data science methods fall short in identifying, allowing companies like EDP Portugal to build a predictive model that can identify potential churners and provide insights into the drivers of churn. The model has been shown to have a recall of 70% and can be integrated into CRM platforms to enable personalized actions to retain customers. By retaining just 10.5k out of 300k churners, companies could potentially retain over $10M in annual revenue.

CARTOframes 1.0 is a Python library that enables Data Scientists and Analysts to integrate CARTO maps data and analysis into their workflows, reducing the time spent on tedious tasks such as data gathering and cleaning. The library has been built with the OS community in mind for compatibility with SciPy and GeoPandas, and it integrates with Data Observatory, a repository of public and premium spatial data. With CARTOframes, users can perform end-to-end analysis from their Python notebooks, visualize data, get their data ready for analysis, enrich their data with new sources, and share the results with others, all while making complex tasks more efficient and accurate.

The world is undergoing rapid technological advancements, with fields like Spatial Data Science and Location Intelligence experiencing significant growth due to the increasing availability of always-on data sources with a location component. Autonomous vehicles are expected to leave their incubation phase and become fully realized in the coming years, leading to investment in major Location Intelligence players and impacting numerous industries such as retail, ecommerce, urban planning, and transit decision making. Human augmentation technology is projected to have a significant impact on human life, blurring the lines between science fiction and science fact, and will likely bring new data streams that can be used for deeper analysis and decision making. Commercial drones are proliferating across industries, providing access and efficiency, while digital twin technology involves creating replicas of real-world entities in a digital space, allowing for simulated choice and optimized real-world decision making. These technologies are expected to have profound impacts on various aspects of life, providing businesses, governments, and organizations with new tools and data to improve outcomes and make life better for citizens.