April 2022 Summaries

The Twin Areas analysis is a tool used in site selection applications to compare the characteristics of target locations with an existing site, such as a top-performing store. This approach involves three main steps: selecting relevant variables, gridifying and enriching the location of the origin and target sites, and deriving a similarity score between the origin and each target location. The analysis can be run in Google BigQuery using CARTO's Analytics Toolbox, which provides a set of User Defined Functions and procedures for running advanced spatial analytics natively in BigQuery. By applying this approach, retailers and CPG brands can easily discover the best locations to expand or optimize their network without prior knowledge of the area, optimizing their site planning process.

The retail landscape of a city is complex, with store success depending heavily on location and proximity to competitors. Clothing stores can benefit from being part of a cluster, as customers prefer to visit areas with multiple stores for product comparison. Local Outlier Factor (LOF) analysis is a tool used to understand spatial patterns in these clusters, identifying areas that are densely populated and those that are not. By analyzing LOF scores, retailers can identify key locations, monitor emerging trends, and make data-driven decisions. The analysis also highlights the importance of considering surrounding areas, as administrative boundaries do not always physically exist. The tool has been successfully applied to a study of clothing stores in Washington D.C., revealing insights into store locations and their corresponding LOF scores. The impact of different k-numbers on results is also discussed, with smaller values producing more localized analysis.

Using geospatial data analysis, companies like 1520 Fridge No More JOKR and BUYK are expanding their local grocery delivery services in New York City by optimizing dark store locations for convenience and profitability. Dark stores are fulfillment centers that don't offer in-store pickup or shopping, instead functioning as warehouses to fulfill online grocery orders. With online grocery orders driving 12.3% of all sales and delivery experiencing a 20% year-over-year revenue increase, more grocers are incorporating the dark store model into their expansion plans. Using geospatial data from CARTO Data Observatory and Snowflake Data Marketplace, Expansion Managers can analyze factors like population density, consumer segments, and spatial attributes to find optimal locations for ghost grocery stores in Seattle, Washington, covering 82% of current app sign-ups with a combination of location-allocation analysis and suitability index scores.

In retail site selection, Hy-Vee uses commercial hotspot analysis to identify areas of high commercial activity where their direct competitors are not present, making them potential expansion opportunities. By applying the COMMERCIAL_HOTSPOTS procedure from the Analytics Toolbox for BigQuery, Hy-Vee can find locations with a high level of commercial activity, such as population density and points of interest, that are suitable for new stores. The analysis uses publicly available data from the Iowa Liquor Sales dataset and CARTO Spatial Features dataset to identify areas where there is a significant difference between the value of input variables and the average, indicating high commercial activity. This approach allows retailers to make data-driven decisions about store locations, without relying on predictive models that require large datasets.

Working Toward a Sustainable Future with CARTO & Google Cloud` CARTO and Google Cloud have formed a partnership to help businesses and communities make strategic decisions on sustainability, leveraging technology and data to tackle climate and environmental challenges. The partnership includes other leading companies to deliver scalable solutions for spatial analysis, machine learning, and visualization. A new whitepaper highlights how the ecosystem can accelerate sustainability efforts through advanced analytics and best practices. CARTO brings geospatial capabilities to Google Cloud, trusted by organizations such as Greenpeace and the World Bank, to help reveal actionable insights on lowering carbon footprints and informing resilience planning.

The CARTO platform has released several new features and enhancements in Q1 2022, including a ground-up cloud native development that allows users to solve spatial problems without data leaving their own cloud infrastructure. The platform now offers improved legends and export capabilities for interactive maps in Builder, as well as geocoding and isolines services natively in cloud data warehouse platforms. Users can also visualize massive spatial datasets directly from Snowflake and Redshift, and unlock the power of Spatial SQL for retail analytics with new functions and stored procedures. Additionally, CARTO has released new versions of its deck.gl and React integrations, and a more flexible and secure deployment option using Kubernetes self-hosted.

The CARTO Self Hosted solution on Kubernetes, in partnership with VMware, offers a secure, convenient, and scalable cloud-native Location Intelligence platform for enterprises, enabling them to manage their user data more effectively amidst stricter regulations. This deployment method provides numerous benefits, including security at every layer of the stack, a modern architecture that allows for faster updates and more features, scalability by design, flexible deployment options, and availability in all major clouds and managed Kubernetes clusters. By partnering with VMware, CARTO can provide customers with a secure and reliable solution that meets the highest security requirements, while also offering easy maintenance protocols and convenient installation processes.