Abstract: In this training session, we will explore the utilization of low-code/no-code visual programming platforms to effectively integrate geospatial analysis with a variety of AI algorithms, including machine learning, deep learning, and Explainable AI. Designed primarily for data science novices, this training enables participants to easily embark on their journey without needing extensive programming expertise. They will learn to harness the platform for advanced spatial analysis and the development of sophisticated AI models.
Room S003, CGIS South building (1730 Cambridge St., Cambridge)
Students in GOV 1013: GIS Analysis of Hazard Vulnerability (https://gis.harvard.edu/gov-1013-gis-analysis-hazard-vulnerability), led by Dr. Connie Chen, will be presenting their final projects. The presentations will cover topics such as hazards, vulnerability, resilience, and communities. We welcome scholars interested in these topics and GIS to attend the event, and students will be available for questions and...
The CGA's Jeff Blossom will join the Social Sciences Council data services colleagues from around the University on Thursday, December 7th at 1:30 p.m. for the Current State of Data Services at Harvard presentation.
Hear about the ongoing work, service offerings, and special projects happening in our libraries. The event will feature short presentations followed by a moderated panel discussion.
Shaich Family Alumni and Student Engagement Center (ASEC), Room 202, Clark University
This is Wendy Guan's presentation as a panelist on the Societal Impacts of Geospatial Analytics panel, in the Clark Center for Geospatial Analytics Strategic Launch Workshop.
For more information about the event, please visit: ...
By: Xiaokang Fu, Devika Kakkar, Junyi Chen, Katie Moynihan, Thomas Hegland, Jeff Blossom
Abstract: Travel time estimation is crucial for several geospatial research studies, particularly healthcare accessibility studies. This paper presents a comparative study of six methods for drive time estimation on geospatial big data in the USA. The comparison is done with respect to the cost, accuracy, and scalability of these methods. The six methods examined are Google Maps API, Bing Maps API, Esri Routing Web Service, ArcGIS Pro Desktop, OpenStreetMap NetworkX (OSMnx), and...
Dr. Jacqueline Le Moigne, NASA AIST, who funded the first cohort of 14 digital twin projects among the US federal programs. "Earth System Digital Twins", ...
Promoting well-being is one of the key targets of the Sustainable Development Goals at the United Nations. Many national and city governments worldwide are incorporating subjective well-being (SWB) indicators into their agenda to complement traditional objective development and economic metrics. In this study, we develop the Twitter sentiment geographical index (TSGI), a proxy for SWB by applying natural language processing techniques on a comprehensive archive of 7.4 billion geotagged tweets, posted from 2012 to the present. In contrast to the previous works...
Carbon storage in terrestrial ecosystems and its changes have become the focus of research on climate and environmental changes, wherein forestland changes are the key driving factors affecting it. Studying the relationship between forestland change and carbon storage is helpful to better understand the impact mechanism of the...
The ongoing spread of COVID-19, the war in Ukraine and the intensification of extreme climate events around the world are changing the world, which is also affecting cities participating in the Belt and...
This talk describe CGA's big data tools on New England Research Cloud particularly the Billion Object Platform v2.0. Details here: https://massopen.cloud/2023-workshop/
In: Urban Data Science Lab, School of Architecture, Texas A&M University
Abstract: In the era of the 4th industrial revolution, place and time are embedded in data from many sources, critical in solving global problems. However, spatiotemporal data handling is often costly and time consuming, requiring advanced skills. Analytical results are hard to reuse and difficult to share among researchers with different domain knowledge and varying technical skills. The Spatial Data Lab project (SDL) is aimed at solving these problems by re...
Presenters: Wendy Guan, Lingbo Liu and Tobias Koetter
In: KNIME Fall Summit 2022
Abstract: Organizations around the world depend on geospatial data for site selection, supply chain optimization, environment management, fraud detection and more. Historically, accessing and analyzing this invaluable data, however, has required niche expertise, demanding deep experience with a wide array of data sources and libraries, as well as coding skills. Harvard’s Center for Geographic Analysis & KNIME have teamed up to enable non-expert users to unlock the...