What an AI agent reads on Xi Liu
An assistant's crawler fetches the page once, without running JavaScript, and reads the text, the structured data and the links. Below is what it gets from xiliu.city, taken from the copy the scanner kept.
2,424
characters of text without JavaScript
none
structured data for the business
0
of 3 AI crawlers turned away at the edge
The words an agent gets
Title: Xi Liu · language en. First 1,800 of 2,424 characters:
Xi Liu Toggle navigation Xi Liu Research Publication Contact About I am Xi Liu, a PhD candidate in Geography (GIScience focus) and Social Data Analytics at Penn State. My advisor is Prof. Clio Andris, and I am part of the Friendly Cities Lab. I am also a graduate affiliate of GeoVISTA Center. Before coming to Penn State, I worked in Geosoft Lab with Prof. Yu Liu and received my Master’s degree from Peking University. I also hold a Bachelor’s degree in GIS from Tongji University with a minor in Economics from Fudan University. I spent the past summers in research teams at Nokia Bell Labs and Google. In my studies, I explore data-driven models of human activities to understand cities and solve urban issues. My research interests include urban computing, social/spatial networks, place based data analytics, human mobility, and complexity in cities. My current research focuses on learning dense vector representations for geographic entities, such as points of interest, user trajectories, and location-based social networks, which can generalize high-level features, reveal latent variables, and serve as better input for machine learning models. Please check out my work on research page, publication page, or Google Scholar. Just email me if you are interested in the research topics or future collaborations. In my spare time, I enjoy traveling, cooking, and photography. I also like playing tennis and hiking. Recent news Feb 2019: We have two new papers coming out! One applied transfer learning to analyze global and neighborhood-level residential ornamentation patterns, and the other used representation learning to measure spatial context patterns of places across cities. Please check the details on the research page. Oct 2018: We are organizing a paper session "Challenges in Dee
What it can say about the business
| A business type in JSON-LD | no |
| Name | no |
| Telephone | no |
| Address | no |
| Opening hours | no |
| Menu or price list | nothing |
| Cuisine in structured data | no |
| JSON-LD blocks | 0 |
The links it would act on
No booking or ordering link found. An agent has nothing to act on.
Everything here comes from fetching the homepage once as a crawler. The report turns it into a score and a list of fixes, with code to paste.