Posts by

Giulia Carella

Giulia Carella
Giulia Carella is a Data Scientist at CARTO. She holds a PhD in Applied Statistics and has experience in the development and application of statistics and machine learning methods for spatio-temporal data, with applications ranging from climate science to spatial demography.
Using Spatial Composites for Climate Change Impact Assessment

Using Spatial Composites for Climate Change Impact Assessment

Learn how to assess climate change risks with spatial composites; measure impacts and support decision-making for infrastructure & natural resources.

Spatial Data Science
How to Use Spatial Data to Create a Wildfire Risk Map

How to Use Spatial Data to Create a Wildfire Risk Map

Learn how organizations can predict & prepare for future wildfire risks using Spatial Data & analysis in our latest blog post with WeatherSource.

Spatial Data Science
Locating High Performing Retail Expansion Sites

Locating High Performing Retail Expansion Sites

The Retail module in our Analytics Toolbox for Google BigQuery now includes Twin Areas analysis, an essential tool in Site Selection analytics. Learn more.

Cloud Native
Distance-based functions for the spatial analysis of point data in BigQuery

Distance-based functions for the spatial analysis of point data in BigQuery

We are pleased to introduce 3 new functions for point data analysis in our Analytics Toolbox for Google Cloud BigQuery: K-nearest neighbors, Local Outlier Factor, & G-function

Use cases
Geographically weighted regression for spatial analysis in BigQuery

Geographically weighted regression for spatial analysis in BigQuery

We are pleased to announce that the CARTO Analytics Toolbox for BigQuery now supports the Geographically Weighted Regression method–read about its usage.

Spatial Data Science
Uncovering Site Selection Strategies using Point of Interest Data

Uncovering Site Selection Strategies using Point of Interest Data

Discover how Point of Interest (POI) data can be used to uncover site selection strategies of leading brands in the US including McDonald's, Starbucks, & Subway

Use cases
How Socioeconomic Factors relate to Mobility during COVID-19

How Socioeconomic Factors relate to Mobility during COVID-19

A detailed geospatial analysis of how changes & regional disparity in human mobility during the COVID-19 lockdown can be related to socioeconomic indicators.

Spatial Data Science
COVID-19 Inequality in the US: How Coronavirus is killing more Black Americans

COVID-19 Inequality in the US: How Coronavirus is killing more Black Americans

Using data from the COVID Racial Data Tracker we calculate the mortality risk from COVID-19 for Black Americans & visualize the results.

Spatial Data
WorldPop in CARTO: global demographic insights at high granularity

WorldPop in CARTO: global demographic insights at high granularity

WorldPop global demographic data is now available in our Data Observatory for spatial analysis at fine spatial resolution.

Spatial Data
Why Lockdown Matters: Exploring Human Mobility Data in Italy

Why Lockdown Matters: Exploring Human Mobility Data in Italy

Analyzing the relationship between human mobility and the spread of COVID-19 in Italy.

Use cases
Magnify your Analysis: Statistical Downscaling to Enhance Spatial Resolution

Magnify your Analysis: Statistical Downscaling to Enhance Spatial Resolution

From understanding the dynamics of a business, to modelling physical and biological processes, selecting the proper spatial scale matters.

Spatial Data Science
Building a Recommendation System for Site Planning

Building a Recommendation System for Site Planning

Spatial Data Science