Yue Lin

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Verified

Yue verified their affiliation via an institutional email.

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• Doctor of Philosophy
• Assistant Instructional Professor of GIScience at University of Chicago

Small area estimation is critical for a wide range of applications, including urban planning, funding distribution, and policy formulation. Individual-level population data, which typically include each individual's socio-demographic characteristics and small area location, are a rich source of information for small area estimation. However, indivi...

Privacy has been an important topic within the geospatial science community, particularly driven by the widespread adoption of geospatial technologies such as mobile devices and the vast amount of location data they generate. This has sparked considerable interest in location privacy that is specifically dedicated to the protection of location info...

Geodemographic classification, a process of categorizing neighborhoods into distinct groups based on their demographic, social, and economic characteristics to create summary profiles, has significantly expanded its applications over the last forty years, from its origins in urban sociology to fields such as health, transportation, and public polic...

Artificial intelligence (AI) is catalyzing growing disruptions in contemporary cartography and beyond. Unlike previous mapping technologies, the current wave of AI enables producing maps without explicit programmed rules, which extends and, in some cases, surpasses human intelligence. This transformative capacity has the potential to reshape not on...

Geodemographic analysis clusters geographic areas into socio-demographically homogeneous groups. Existing clustering methods prioritize overall effectiveness, measured by total costs, potentially misrepresenting specific subgroups. Despite a growing literature on fair clustering, it largely focuses on crisp clustering, failing to address the inhere...

Spatial aggregation is essential for applications where data at low level spatial units such as census blocks are grouped into larger regions. This type of problem can be formulated as spatial optimization problems where the goal is to minimize the difference between the grouped regions. These problems are difficult to solve because of their comput...

Privacy and utility are two important objectives to consider when releasing census data. However, these two objectives are often conflicting, as protecting privacy usually necessitates introducing noise into the data, which compromises data utility. Determining the appropriate level of privacy protection presents a significant challenge in the data...

Spatial point mapping is a useful practice in exploratory point pattern analysis, but it poses significant privacy risks as the identity of individuals may be revealed from the maps. Geomasking methods have been developed to mitigate the risks by displacing spatial points before mapping. However, many of these methods rely on a weak privacy notion...

Geographically aggregated demographic, social, and economic data are valuable for research and practical applications, but their use and sharing often compromise individual privacy. The U.S. Census Bureau has responded to this issue by introducing a new privacy protection method, the TopDown Algorithm (TDA), in the 2020 Census. The TDA is based on...

The modifiable areal unit problem (MAUP) can significantly impact the use of census data as different choices in aggregating geographic zones can lead to varying outcomes. Previous research studied the effects using random aggregations, which, however, may lead to the use of impractical and unrealistic zones that deviate from recommended census geo...

Sanborn Fire Insurance maps contain a wealth of building-level information about U.S. cities dating back to the late 19th century. They are a valuable resource for studying changes in urban environments, such as the legacy of urban highway construction and urban renewal in the 20th century. However, it is a challenge to automatically extract the bu...

Spatial prediction is commonly used in social and environmental research to estimate values at unobserved locations using sampling data. However, most existing spatial prediction methods and software packages are based on the assumption of spatial autocorrelation (SAC), which may not apply when spatial dependence is weak or non-existent. In this ar...

Small area microdata contain attributes and locations of individual members of a population in small census geographies. This type of data is critical in research and policymaking, but it is often not publicly available due to confidentiality concerns. The limited access to small area microdata can result in insufficient data for certain research (...

Geographically aggregated data are often considered to be safe because information can be published by group as population counts rather than by individual. Identifiable information about individuals can still be disclosed when using such data, however. Conventional methods for protecting privacy, such as data swapping, often lack transparency beca...

The growing number of real-time camera feeds in urban areas has made it possible to provide high-quality traffic data for effective transportation planning, operations, and management. However, deriving reliable traffic metrics from these camera feeds has been a challenge because of the limitations of current vehicle detection techniques, as well a...

The purpose of this paper is to describe the development of a synthetic population dataset that is open and realistic and can be used to facilitate understanding the cartographic process and contextualizing the cartographic artifacts. We first discuss an optimization model that is designed to construct the synthetic population by minimizing the dif...

The growing number of real-time camera feeds in urban areas has made it possible to provide high-quality traffic data for effective transportation planning, operations, and management. However, deriving reliable traffic metrics from these camera feeds has been a challenge due to the limitations of current vehicle detection techniques, as well as th...

Severe air pollution has significantly impacted climate and human health worldwide. In this study, global and local Moran’s I was used to examine the spatial autocorrelation of PM2.5 pollution in North China from 2000–2017, using data obtained from Atmospheric Composition Analysis Group of Dalhousie University. The determinant powers and their inte...

Assessing the association between maps is a basic task in GIS. It not only helps to discover the consistency between mapped distributions and to reveal the relationship between various spatial patterns, but also implies attribution. Although literatures are rich in the map association measures, their applications are still tricky because of the com...

Address matching is a crucial step in geocoding, which plays an important role in urban planning and management. To date, the unprecedented development of location-based services has generated a large amount of unstructured address data. Traditional address matching methods mainly focus on the literal similarity of address records and are therefore...

Standard address data are essential geographical information that play an important role in urban management. However, due to the complex structures of Chinese addresses, poor address quality has long been a problem in China. Although several measures were established to improve the address quality, nonstandard address data are still common in new...

Urban landmarks are of significant importance to spatial cognition and route navigation. However, the current landmark extraction methods mainly focus on the visual salience of landmarks and are insufficient for obtaining high extraction accuracy when the size of the geographical dataset varies. This study introduces a random forests (RF) classifie...