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Our lab member, Jimin Wang, recently completed his MS in GIS degree. His master project focuses on Advancing Spatial and Textual Analysis with GeoAI. Particularly, Jimin has published three related papers on this topic, which are:

• Jimin Wang and Yingjie Hu (2019): Enhancing spatial and textual analysis with EUPEG: an extensible and unified platform for evaluating geoparsers. Transactions in GIS, 23(6), 1393-1419. [PDF]
• Jimin Wang and Yingjie Hu (2019): Are we there yet? Evaluating state-of-the-art neural network based geoparsers using EUPEG as a benchmarking platform, In: Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Geospatial Humanities, Nov. 5, Chicago, USA. [PDF]
• Jimin Wang, Yingjie Hu, and Kenneth Joseph (2020): NeuroTPR: A Neuro-net ToPonym Recognition model for extracting locations from social media messages. Transactions in GIS, accepted. [PDF]

Jimin’s master committee members are Dr. Yingjie Hu and Dr. Enki Yoo. In addition, Dr. Kenneth Joseph also provided guidance to Jimin’s research.

Moving forward, Jimin has received a fellowship package from UB’s PhD Excellence Initiative which aims to “recruiting the very best PhD students and providing them with transformative academic programs that prepare them for future success”. Jimin will continue his study as a PhD student in our GeoAI Lab, and we look forward to his new achievements in the coming years.

Congratulations again, Jimin!

Abstract: Social media messages, such as tweets, are frequently used by people during natural disasters to share real-time information and to report incidents. Within these messages, geographic locations are often described. Accurate recognition and geolocation of these locations is critical for reaching those in need. This paper focuses on the first part of this process, namely recognizing locations from social media messages. While general named entity recognition (NER) tools are often used to recognize locations, their performance is limited due to the various language irregularities associated with social media text, such as informal sentence structures, inconsistent letter cases, name abbreviations, and misspellings. We present NeuroTPR, which is a Neuro-net ToPonym Recognition model designed specifically with these linguistic irregularities in mind. Our approach extends a general bidirectional recurrent neural network model with a number of features designed to address the task of location recognition in social media messages. We also propose an automatic workflow for generating annotated datasets from Wikipedia articles for training toponym recognition models. We demonstrate NeuroTPR by applying it to three test datasets, including a Twitter dataset from Hurricane Harvey, and comparing its performance with those of six baseline models.

Full paper: Jimin Wang, Yingjie Hu, and Kenneth Joseph (2020): NeuroTPR: A Neuro-net ToPonym Recognition model for extracting locations from social media messages. Transactions in GIS, accepted. [PDF]

Figure 1: The two steps of geoparsing in the context of disaster response and our focus on toponym recognition.

Figure 2: The overall architecture of NeuroTPR.

Andris, C., Ayers, E., Grossner, K., Hu, Y., Hart, K., Thatcher, J., Tally Jr, R.T. and Giordano, A., 2020. Towards Geospatial Humanities: Reflections from Two Panels. International Journal of Humanities and Arts Computing, 14(1-2), pp.6-26. [PDF]

This paper is based on the panel discussion at the 2019 UCGIS Symposium on the Geospatial Humanities. We discussed the opportunities and challenges for conducting interdisciplinary research integrating GIScience and humanities as well as preparing students with necessary data analysis and visualization skills for geospatial humanities work. Very interesting panel discussion and a lot of research possibilities!

With the outbreak of COVID-19, we are deeply concerned about the safety and health of many GIScience students and scholars in Wuhan and in China. Along with UCSB STKO Lab and UW-Madison GeoDS Lab, we made this video to support Wuhan and China. Stay strong!

“The Austrian Academy of Sciences’ Commission for GIScience annually selects the winner of a ‘Young Researcher’ competition, based on an outstanding publication submitted by applicants enhancing the body of literature in Geoinformatics and GIScience.”

Dr. Yingjie Hu received this year’s award for his publication:
Yingjie Hu (2018): Geo-text data and data-driven geospatial semantics. Geography Compass, 12(11), e12404. [PDF]

Yingjie greatly appreciates this recognition and looks forward to making more contributions to GIScience.

Link to the original blog article: http://gisciencecommission.blogspot.com/2020/01/2020-waldo-tobler-young-researcher.html

Geospatial artificial intelligence (GeoAI) is an interdisciplinary field that has received tremendous attention from both academia and industry in recent years. We recently published an article that reviews the series of GeoAI workshops held at the Association for Computing Machinery (ACM) International Conference on Advances in Geographic Information Systems (SIGSPATIAL) since 2017. These workshops have provided researchers a forum to present GeoAI advances covering a wide range of topics, such as geospatial image processing, transportation modeling, public health, and digital humanities. We provide a summary of these topics and the research articles presented at the 2017, 2018, and 2019 GeoAI workshops. We conclude with a list of open research directions for this rapidly advancing field.

Full article: Yingjie Hu, Song Gao, Dalton Lunga, Wenwen Li, Shawn Newsam, and Budhendra Bhaduri (2019): GeoAI at ACM SIGSPATIAL: progress, challenges, and future directions, ACM SIGSPATIAL Special, 11(2), 5-15. [PDF]

Proceedings of the GeoAI workshops in 2017, 2018, and 2019 are available here:

• Proceedings of the 2019 ACM SIGSPATIAL Workshop on GeoAI
• Proceedings of the 2018 ACM SIGSPATIAL Workshop on GeoAI
• Proceedings of the 2017 ACM SIGSPATIAL Workshop on GeoAI

Are We There Yet? Evaluating State-of-the-Art Neural Network based Geoparsers Using EUPEG as a Benchmarking Platform

Abstract: Geoparsing is an important task in geographic information retrieval. A geoparsing system, known as a geoparser, takes some texts as the input and outputs the recognized place mentions and their location coordinates. In June 2019, a geoparsing competition, Toponym Resolution in Scientific Papers, was held as one of the SemEval 2019 tasks. The winning teams developed neural network based geoparsers that achieved outstanding performances (over 90% precision, recall, and F1 score for toponym recognition). This exciting result brings the question “are we there yet?”, namely have we achieved high enough performances to possibly consider the problem of geoparsing as solved? One limitation of this competition is that the developed geoparsers were tested on only one dataset which has 45 research articles collected from the particular domain of Bio-medicine. It is known that the same geoparser can have very different performances on different datasets. Thus, this work performs a systematic evaluation of these state-of-the-art geoparsers using our recently developed benchmarking platform EUPEG that has eight annotated datasets, nine baseline geoparsers, and eight performance metrics. The evaluation result suggests that these
new geoparsers indeed improve the performances of geoparsing on multiple datasets although some challenges remain.

Jimin Wang & Yingjie Hu (2019): Are We There Yet? Evaluating State-of-the-Art Neural Network based Geoparsers Using EUPEG as a Benchmarking Platform, In: Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Geospatial Humanities, Nov. 5, Chicago, USA. [PDF]

What is the current state-of-the-art in integrating results from artificial intelligence research into geographic information science and the earth sciences more broadly? Does GeoAI research contribute to the broader field of AI, or does it merely apply existing results? What are the historical roots of GeoAI? Are there core topics and maybe even moonshots that jointly drive this emerging community forward? We answer these questions in our recent editorial by providing an overview of past and present work, explain how a change in data culture is fueling the rapid growth of GeoAI work, and point to future research directions that may serve as common measures of success.

The full GeoAI editorial on IJGIS:
Janowicz, K., Gao, S., McKenzie, G., Hu, Y. & Bhaduri, B. (2020): GeoAI: Spatially explicit artificial intelligence techniques for geographic knowledge discovery and beyond, International Journal of Geographical Information Science, 34(4), 625-636. [PDF]

The entire special issue can be accessed here.

A new paper led by GeoAI lab member Jimin Wang, “Enhancing spatial and textual analysis with EUPEG: An extensible and unified platform for evaluating geoparsers”, is accepted by the journal Transactions in GIS: https://onlinelibrary.wiley.com/doi/10.1111/tgis.12579

Abstract: A rich amount of geographic information exists in unstructured texts, such as web pages, social media posts, housing advertisements, and historical archives. Geoparsers are useful tools that extract structured geographic information from unstructured texts, thereby enabling spatial analysis on textual data. While a number of geoparsers have been developed, they have been tested on different data sets using different metrics. Consequently, it is difficult to compare existing geoparsers or to compare a new geoparser with existing ones. In recent years, researchers have created open and annotated corpora for testing geoparsers. While these corpora are extremely valuable, much effort is still needed for a researcher to prepare these data sets and deploy geoparsers for comparative experiments. This article presents 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. The main objective of EUPEG is to reduce the time and effort that researchers have to spend in preparing data sets and baselines, thereby increasing the efficiency and effectiveness of comparative experiments.

Online demo: https://geoai.geog.buffalo.edu/EUPEG
Code and data: https://github.com/geoai-lab/EUPEG

The input and output of geoparsing and its two main steps

The overall architecture of EUPEG.

A screenshot of EUPEG and the (1)–(2)–(3) workflow for running an experiment

Running time of different geoparsers on GeoCorpora.

An illustration of the AUC for quantifying the overall error distance of a geoparser.

The perceptions of people toward neighborhoods reveal their satisfactions with their living environments and their perceived quality of life. Recently, there is an emergence of websites designed for helping people to find suitable places to live. On these websites, current and previous residents can review their neighborhoods by providing numeric ratings and textual comments. Such online neighborhood review data provide novel opportunities for studying the perceptions of people toward their neighborhoods. In this work, we analyze such online neighborhood review data. Specifically, we extract two types of knowledge from the data: 1) semantics, i.e., the semantic topics (or aspects) that people talk about their neighborhoods; and 2) sentiments, i.e., the emotions that people express toward the different aspects of their neighborhoods. We experiment with a number of different computational models in extracting these two types of knowledge and compare their performances. The experiments are based on a dataset of online reviews about the neighborhoods in New York City (NYC), which were contributed by 7,673 distinct Web users. We also conduct correlation analyses between the subjective perceptions extracted from this dataset and the objective socioeconomic attributes of NYC neighborhoods, and find similarities and differences. The effective models identified in this research can be applied to neighborhood reviews in other cities for supporting urban planning and quality of life studies.

More details about this work can be found in our full paper: Yingjie Hu, Chengbin Deng, and Zhou Zhou (2019): A semantic and sentiment analysis on online neighborhood reviews for understanding the perceptions of people toward their living environment. Annals of the American Association of Geographers, 109(4), 1052-1073. [PDF]

(a) Some neighborhood reviews on Niche; (b) average ratings of NYC neighborhoods based on Niche review data.

Eight semantic topics discovered from the online reviews using LDA.

Average neighborhood perception maps for the eight semantic topics using LARA.

Media coverage about this work: