How to Use Spatial Data Science in your Site Planning Process? [CARTOframes]
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The document outlines the use of spatial data science in site planning, focusing on how Carto can turn location data into actionable business outcomes through a comprehensive journey of data management, enrichment, analysis, and visualization. It emphasizes the integration of various data sources, including demographics, traffic patterns, and financial data, to inform investment and operational decisions. Additionally, the document discusses challenges related to data sampling, anonymization, and variability, and introduces tools like CartoFrames for seamless data analysis workflows.
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How to Use Spatial Data Science in your Site Planning Process? [CARTOframes]
- 1. How to Use Spatial Data Science in Your Site Planning Process FOLLOW @CARTO ON TWITTER
- 2. The Sum of Our Parts Today’s Speakers Giulia Carella Steve Isaac Data Scientist Content Marketing Manager
- 3. CARTO — Turn Location Data into Business Outcomes CARTO is the platform to build powerful Location Intelligence apps with the best data streams available.
- 4. CARTO Customers Pioneers in Location Intelligence 1,200 End-users 300K Team members 100+
- 5. CARTO — Turn Location Data into Business Outcomes The Complete Journey 1. Data Ingestion & Management 2. Enrichment 3. Analysis 4. Solutions & Visualization 5. Integration
- 6. CARTO — Turn Location Data into Business Outcomes The Complete Journey 1. Data Ingestion & Management 2. Enrichment 3. Analysis 4. Solutions & Visualization 5. Integration
- 7. Enrichment ● Save time in gathering spatial data, augmenting your existing data with demographics from across the globe ● Create locations from addresses and understand travel time all from within CARTO ● Develop robust ETL processes and update mechanisms so your data is always enriched ● Premium data to understand and analyze deeper trends and behavior Data Observatory ETL Processing CARTO Grid Data Services API Routing & Traffic Geocoding
- 8. Analysis ● Bring maps and data into your Data Science workflows and the Python data science ecosystem with CARTOframes ● Machine learning embedded in CARTO as simple SQL calls for clustering, outliers analysis, time series predictions, and geospatial weighted regression ● Use the power of PostGIS and our APIs to productionalize analysis workflows in your CARTO platform CARTO Frames Analysis API SQL API Python SDK
- 9. Spatial Data Science for Site Planning
- 10. Financial Housing Human Mobility Road Traffic Points of Interest Demographics Merchant and ATM transaction data from leading banks and credit card companies Mobile device and GPS data provide insight into human movement patterns The most recent census data including: age, income, household types and more Property statistics, prices, and history to drive decisions in investment portfolios Data from routing apps and GPS to analyse traffic patterns and commuter behaviour Location data for business establishments, restaurants, schools, attractions, and more
- 11. CARTO — Turn Location Data into Business Outcomes The Age of Data Abundance?
- 12. AND ITS HIDDEN PITFALLS Sampling Bias Data may not be collected using random samples, e.g. need extrapolation to the total population
- 13. AND ITS HIDDEN PITFALLS Sampling Bias Data may not be collected using random samples, e.g. need extrapolation to the total population Anonymisation Data needs to be anonymised to meet regulations, and vendors have different approaches for that
- 14. AND ITS HIDDEN PITFALLS Sampling Bias Data may not be collected using random samples, e.g. need extrapolation to the total population Anonymisation Data need to be anonymised to meet regulations, and vendors have different approaches for that Different Aggregations Data comes in different spatial aggregations such as grid cells of different sizes or administrative boundaries
- 15. Financial Grid 110x110m POI Points aggregated on a 70x70m grid Demographics Census tracts
- 16. Building a Common Reference Grid
- 17. Which spatial scale is correct? How do we change from one spatial scale to another? THE CHANGE OF SUPPORT PROBLEM Statistical downscale/upscale model to DISAGGREGATE/AGGREGATE the data at different spatial resolutions
- 18. A PRELIMINARY SOLUTION AREA WEIGHTENING Which spatial scale is correct? How do we change from one spatial scale to another?
- 19. Exploring the available data: CARTO DATA OBSERVATORY
- 20. Viz using vector maps Connector to CARTO platform WHAT IS CARTOframes? ● Python package ● To be used in Jupyter Notebooks ● Built for Data Scientists ● Part of CARTO Analysis stack CARTOFrames Analysis API SQL API Python SDK
- 22. Quadkeys https://docs.microsoft.com/en-us/bingmaps/articles/bing-maps-tile-system
- 26. Defining Similarity for Site Planning
- 27. CARTO — Turn Location Data into Business Outcomes WITH SOME CAVEATS: 1. Different variances? 2. Correlated variables? 3. Missing data? 4. When is a distance small enough? Or how to define similarity? TWIN AREA MODEL
- 31. 1. Eigen-decomposition of the sample covariance matrix 2. Rearrange the columns in the eigenvector matrix in order of decreasing eigenvalue 3. Keep only the eigenvectors that correspond to the p-largest eigenvalues 4. Compute the principal components (PC) 5. Reconstruct the original data How many PCs? Let’s use an ensemble!
- 32. MISSING DATA
- 33. 1. PCA can also be described as the ML solution of a probabilistic latent variable model (PPCA) 2. Find the ML estimate for the model parameters using the EM algorithm 2.1. E-step: 2.2. M-step
- 34. Similarity Score HOW TO DEFINE SIMILARITY So far we have only computed distances in the variable space 0 1 Actually since we are computing an K-ensemble of distances... Let’s compare instead the score for each target location to the score from the mean vector data
- 35. Takeaways CARTO Data Observatory (DO) for data enrichment CARTOframes as a connector to the DO and for powerful vector visualizations Site-planning applications require various sources of location data streams Easily derive data-driven insights when opening, relocating or consolidating location sites
- 36. Thanks for listening! Any questions? Request a demo at CARTO.COM Giulia Carella Data Scientist // giulia@carto.com Steve Isaac Content Marketing Manager // sisaac@carto.com