Yizhao Gao
Biographic Data
| ID | 3635394 |
|---|---|
| NAME | Yizhao Gao |
| GIVEN NAMES | Yizhao |
| FAMILY NAME | Gao |
| SIGNATURE | GAO Y |
| AFFILIATIONS | University of Illinois Urbana-Champaign |
| ORCID | 0000-0002-4564-3646 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2020 |
| LATEST PUBLICATION YEAR | 2020 |
| H-INDEX | 0 |
An urban informatics approach to understanding residential mobility in Metro Chicago
This paper proposes that urban informatics can represent a flow of information from diverse and voluminous data into and back from the planning process. We present a proof-of-concept of how an urban informatics approach can improve public understanding of essential information in the planning process, and we propose a theoretical construct of how it can make planning processes more democratic and participatory, especially for disadvantaged groups…
Escip
Detecting irregularly shaped spatial clusters within heterogeneous point processes is challenging because the number of potential clusters with different sizes and shapes can be enormous. This research develops a novel method, expansion-based spatial clustering for inhomogeneous point processes (ESCIP), for detecting spatial clusters of any shape within a heterogeneous point process in the context of analyzing spatial big data. Statistical testin…
No prominent works on this page.
An urban informatics approach to understanding residential mobility in Metro Chicago
This paper proposes that urban informatics can represent a flow of information from diverse and voluminous data into and back from the planning process. We present a proof-of-concept of how an urban informatics approach can improve public understanding of essential information in the planning process, and we propose a theoretical construct of how it can make planning processes more democratic and participatory, especially for disadvantaged groups…
Escip
Detecting irregularly shaped spatial clusters within heterogeneous point processes is challenging because the number of potential clusters with different sizes and shapes can be enormous. This research develops a novel method, expansion-based spatial clustering for inhomogeneous point processes (ESCIP), for detecting spatial clusters of any shape within a heterogeneous point process in the context of analyzing spatial big data. Statistical testin…
Computer Science (2 works) · Geography (2 works) · Human Mobility and Location-Based Analysis (2 works) · Algorithm (1 work) · Artificial Intelligence (1 work) · Cartography (1 work) · Citizen journalism (1 work) · Cluster analysis (1 work) · Data mining (1 work) · Data science (1 work)