Jie Liu
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Jie Liu is a senior product engineer on the Spatial Statistics team. Jie earned her bachelor’s degree in Urban Planning and minored in Economics at Peking University, and earned dual degrees in Master of City Planning and Master of Urban and Spatial Analytics in School of Design, University of Pennsylvania. She dives deep into spatial statistics algorithms but is also design- and user-focused. She loves applying spatial data science to solve transportation planning and socio-economic problems. In her free time, Jie enjoys snowboarding, hiking, backpacking, cooking, and playing the ukulele.

Posts by this author
What’s new in Forest-based Forecast

This article shows the enhancements added to the Forest-based Forecast tool and demonstrates how to integrate this tool into an analysis workflow

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Eight tips to help you make better presence prediction models with Presence-only Prediction (MaxEnt)

Quick tips for tweaking parameters, finding an optimal model, and mastering the Presence-Only Prediction (MaxEnt) tool.

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Presence-Only Prediction (MaxEnt) 101: Using GIS to model species distribution

Give step-by-step instructions on how to run the new Presence-only Prediction (MaxEnt) tool and interpret the results.

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Looking to the future: Using GIS to model and predict population

We’ll share a source of historical county population data and introduce a set of exciting, easy-to-use forecasting tools available in ArcGIS Pro.

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Integrate a spatial approach and time series forecasting

Learn more analysis details about forecasting the change of relationship between PM 2.5 level and population of people of color from 2010 to 2025

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Spatial Data Science at the 2021 Esri Developer Summit

Join us at the 2021 Esri Developer Summit to discover and dive deep into latest development of Spatial Statistics and Space Time Pattern Mining.

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Time Series Forecasting 101 – Part 3. Forecast COVID-19 cumulative confirmed cases with Curve Fit Forecast and Evaluate Forecasts by Location

The third article of the series shows how we can forecast cumulative confirmed cases of COVID-19 at US county level with Time Series Forecasting

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Time Series Forecasting 101 – Part 2. Forecast COVID-19 daily new confirmed cases with Exponential Smoothing Forecast and Forest-based Forecast

The second article of the series shows how we can forecast daily new confirmed cases of COVID-19 at US county level with Time Series Forecasting

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Time Series Forecasting 101 – Part 1. COVID-19 data preparation with ArcGIS Notebooks in ArcGIS Pro

This first article of the Time series forecasting 101 series covers creating a space-time cube of COVID-19 confirmed cases time-series data.

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