July 2023 Summaries

Meet CARTO at Google Next ‘23 in San Francisco, where they will showcase their commitment to cloud-native spatial analytics and innovation. CARTO has achieved Google Cloud Ready - BigQuery status, recognizing their dedication to delivering cutting-edge spatial analysis to Google Cloud users. At the event, Jeremy Flynn will host a session on the role of geospatial data in sustainability, featuring a live demo showcasing CARTO's capabilities. CARTO is also hosting a Tapas & Mapas event, where attendees can connect with Googlers and industry experts over food and wine, and request a free demo to learn more about how their organization can use location intelligence.

Spatial interpolation is a mathematical technique used to estimate values between known data points. It involves constructing a function or curve that passes through the given data points and can be used to predict missing values at intermediate locations. Spatial interpolation is influenced by geographic proximity, making it distinct from non-spatial interpolation methods. Two popular spatial interpolation techniques are Inverse Distance Weighting (IDW) and Kriging. IDW calculates interpolated values based on distances between known points, producing smooth surfaces with gradual transitions, but can be oversensitive to outliers. Kriging, a geostatistical technique, takes into account both distance and spatial correlation of data points, providing estimates of uncertainty and optimized estimation, but is computationally demanding and requires careful model selection. The choice between IDW and Kriging depends on the characteristics of the data and desired outcomes, with IDW suitable for simple, fast interpolation and Kriging better suited for datasets with complex patterns and accurate estimation.

The text discusses the importance of understanding spatial trends in the vacation rental industry to make data-driven decisions. It highlights the use of geospatial analysis to identify patterns behind supply and demand trends, as well as relationships between variables such as hospitality locations, tourist attractions, and proximity to rail stations. The analysis is performed using CARTO's platform, which provides a comprehensive and granular short-term rental database. The study examines the spatial relationship between vacation rentals in the Hovedstaden region of Denmark and various input variables, including hospitality locations, tourist attractions, and proximity to rail stations. The results show localized relationships between variables, with positive values indicating a positive relationship, negative values indicating a negative relationship, and 0 indicating no relationship. The analysis demonstrates the potential of spatial data science tools in driving decision-making across various industries, including real estate investment, tourism industry planning, pricing strategies, urban planning and development, and business expansion.

Tilesets are a game-changer for efficiently visualizing large volumes of geospatial data, allowing for faster loading, optimized bandwidth usage, and seamless navigation. They revolutionize the spatial data rendering process by dividing datasets into smaller, pre-rendered image tiles, ensuring users only load necessary tiles for their zoom level and extent. With tilesets, developers can create detailed maps with minimal performance impact, enabling better insights and a more immersive experience. The process of creating tilesets has become easier, requiring less engineering and geospatial skills, thanks to advancements in technology. Tilesets offer various types, such as aggregation and spatial index tilesets, which provide additional benefits like visualizing trends and distributions at different zoom levels. By leveraging tilesets, developers can unlock the power of big spatial data and create stunning maps with minimal storage costs.

Towards a Carbon Neutral Future with CARTO & NetCarbon ` NetCarbon, a climate-focused scale-up, is working to help farmers and territories measure, improve, and value carbon sequestration using satellite imagery and algorithms. To handle large amounts of satellite data, they adopted a scalable solution that integrated with Google BigQuery, allowing them to connect over 10 terabytes of data in seconds. Leveraging spatial indexing functionality, NetCarbon can compute data in real time, streamlining their workflow. As they continue to grow, they aim to integrate more spatial datasets and work closely with CARTO as a partner to enhance their insights and contribute towards achieving carbon neutrality.

Spatial Spotlights: Our new series of weekly 15-minute webinars featuring experts from the CARTO team, where they share tips and tricks on leveraging H3 for spatial analysis, best practice guidance, and more! These webinars are hosted live on LinkedIn and can be joined from your phone or watched on-demand on YouTube. Helen McKenzie, a Geospatial Advocate, recently kicked off the series by showcasing how to leverage Spatial Indexes along with human mobility data to optimize locations for OOH billboards. This talk focused on determining the optimal location for a CPG company's new product marketing among 269 billboards across London using food and drink spend and footfall levels. The approach could be utilized by many industries, including CPG, retail, and telecoms. Upcoming webinars include sessions from experts on finding locations for telco towers, geomarketing techniques, and leveraging SQL parameters for bike trips in New York.

We've made significant enhancements to the CARTO platform in Q2 2023, focusing on spatial data analysis and visualization capabilities that run natively in cloud data warehouses such as Google BigQuery, Snowflake, Redshift, and Databricks. New features include advanced calculations in CARTO Builder, improved geospatial analysis functions via Analytics Toolbox, and support for space-time cluster analysis, statistical measures, and H3 spatial indexes. We've also simplified API access token management, launched a fleet optimization toolkit with Databricks, and integrated Google Maps Platform's photorealistic 3D tiles into deck.gl, enabling more interactive data exploration experiences and better insights from massive amounts of data.

"Do you need to code for a career in data?" is a question that produces 1.69 billion search results on Google, but the answer depends on various factors such as career goals, industry, and personal preferences. Coding skills are essential for managing and querying relational database management systems, running analysis, and developing custom tools, applications, and workflows tailored to specific needs. However, with the rise of no-code technologies, some tasks can be automated without coding, and AI is set to play a significant role in geospatial data analysis, potentially reducing the need for coding skills. To navigate this landscape, it's recommended to learn the fundamentals of coding languages such as Python, R, JavaScript, and HTML, master what's relevant to your interests and career goals, and stay agile by updating your skills to adapt to changing industry trends. The "big 5" GIS coding languages - Structured Programming Language, Python, R, JavaScript, and HTML - are essential for geospatial professionals, but knowing their functions is key, even if you don't plan on writing code. With the right approach and skills, you can thrive in a career that involves data analysis and science, regardless of whether you choose to code or not.