Projects

This page contains the research projects we have completed so far. In addition to papers, we also provide the code and data produced through these projects.

Extensible and Unified Platform for Evaluating Geoparsers

This project develops EUPEG: an Extensible and Unified Platform for Evaluating Geoparsers. EUPEG is an open source and web‐based benchmarking platform which hosts the majority of open corpora, geoparsers, and performance metrics reported in the literature. It enables direct comparison of the geoparsers hosted, and a new geoparser can be connected to EUPEG and compared with other geoparsers.

Paper: https://onlinelibrary.wiley.com/doi/10.1111/tgis.12579
Code and Data: https://github.com/geoai-lab/EUPEG

Topic Modeling and Sentiment Analysis

The perceptions of people toward neighborhoods reveal their satisfactions with their living environments and their perceived quality of life. In this project, we analyze online neighborhood review data to understand the perceptions of people toward neighborhoods. Specifically, we perform topic modeling to understand the semantic topics that people talk about their neighborhoods, and sentiment analysis to understand the emotions expressed by people.

Paper: https://doi.org/10.1080/24694452.2018.1535886
Code and Data: https://github.com/geoai-lab/TopicAndSentimentAnalysis

Harvesting Local Place Names from Housing Posts

Local place names are frequently used by residents living in a geographic region. Such place names may not be recorded in existing gazetteers. In this work, we propose a computational framework for harvesting local place names from geotagged housing posts. We make use of those posts on local-oriented websites, such as Craigslist, where local place names are often mentioned. The proposed framework consists of two stages: natural language processing (NLP) and multi-scale geospatial clustering.

Paper: https://www.tandfonline.com/doi/abs/10.1080/13658816.2018.1458986
Code and Data: https://github.com/YingjieHu/LocalPlaceName

Place Names and Their Changes with Geographic Distance

In this project, we conduct an empirical study based on 112,071 POIs in seven US metropolitan areas extracted from an open Yelp dataset. We propose to adopt term frequency and inverse document frequency in geographic contexts to identify local terms used in POI names and to analyze their usages across different POI types. Our results show an uneven usage of local terms across POI types. We also examine the decaying effect of POI name similarity with the increase of distance among POIs.

Paper: http://dx.doi.org/10.4230/LIPIcs.GISCIENCE.2018.5
Code and Data: https://github.com/YingjieHu/POI_Name

Semantic Relatedness between Cities via News Articles

This project develops a topic modeling based workflow for extracting semantic relatedness between cities from news articles. News articles contain a rich amount of information about cities and their relations. The developed workflow makes use of city co-occurrences in news articles, and employs a topic model, LLDA, to analyze the full texts of news articles and to extract city relatedness. The extracted semantic relatedness can contribute to applications such as urban planning and GIR.

Paper: http://www.tandfonline.com/doi/abs/10.1080/13658816.2017.1367797
Code and Data: https://github.com/YingjieHu/CityRelatednessViaNews