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Yingjie Hu

SUNY: University at Buffalo, Geography, Faculty Member

University of Tennessee Knoxville, Geography, Faculty Member

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My name is Yingjie Hu. I am an Associate Professor in the Department of Geography at the University at Buffalo (UB). I am also an Adjunct Professor in the Department of Computer Science and Engineering and an Affiliated Professor of the UB AI and Data Science Institute. My major research area is in geographic information science (GIScience), and more specifically in geospatial artificial intelligence (GeoAI) and spatial data science. I lead the GeoAI Lab at UB, where we integrate geospatial data, spatial analysis, and AI methods to understand human-environment interactions and to address some of the challenging problems facing our society, such as those related to natural disasters, public health, and ecosystem conservation. Together, I hope the work of my group can contribute to the sustainable development of our society.

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articles by Yingjie Hu

Extracting and analyzing semantic relatedness between cities using news articles

News articles capture a variety of topics about our society. They reflect not only the socioecono... more News articles capture a variety of topics about our society. They reflect not only the socioeconomic activities that happened in our physical world, but also some of the cultures, human interests, and public concerns that exist only in the perceptions of people. Cities are frequently mentioned in news articles, and two or more cities may co-occur in the same article. Such co-occurrence often suggests certain relatedness between the mentioned cities, and the relatedness may be under different topics depending on the contents of the news articles. We consider the relatedness under different topics as semantic relatedness. By reading news articles, one can grasp the general semantic relatedness between cities; yet, given hundreds of thousands of news articles, it is very difficult, if not impossible, for anyone to manually read them. This paper proposes a computational framework which can 'read' a large number of news articles and extract the semantic relatedness between cities. This framework is based on a natural language processing model and employs a machine learning process to identify the main topics of news articles. We describe the overall structure of this framework and its individual modules, and then apply it to an experimental dataset with more than 500,000 news articles covering the top 100 US cities spanning a 10-year period. We perform exploratory visualizations of the extracted semantic relatedness under different topics and over multiple years. We also analyze the impact of geographic distance on semantic relatedness and find varied distance decay effects. The proposed framework can be used to support large-scale content analysis in city network research.

A data-synthesis-driven method for detecting and extracting vague cognitive regions

Cognitive regions and places are notoriously difficult to represent in geographic information sci... more Cognitive regions and places are notoriously difficult to represent in geographic information science and systems. The exact delineation of cognitive regions is challenging insofar as borders are vague, membership within the regions varies non-monotonically, and raters cannot be assumed to assess membership consistently and homogeneously. In a study published in this journal in 2014, researchers devised a novel grid-based task in which participants rated the membership of individual cells in a given region and contrasted this approach to a standard boundary-drawing task. Specifically, the authors assessed the vague cognitive regions of Northern California and Southern California. The boundary between these cognitive regions was found to have variable width, and region membership peaked not at the most northern or southern cells but at substantially less extreme latitudes. The authors thus concluded that region membership is about attitude, not just latitude. In the present work, we reproduce this study by approaching it from a computational fourth-paradigm perspective, i.e., by the synthesis of high volumes of heterogeneous data from various sources. We compare the regions which we identify to those from the human-participants study of 2014, identifying differences and commonalities. Our results show a significant positive correlation to those in the original study. Beyond the extracted regions themselves, we compare and contrast the empirical and analytical approaches of these two methods, one a conventional human-participants study and the other an application of increasingly popular data-synthesis-driven research methods in GIScience.

Prioritizing Disaster Mapping Tasks for Online Volunteers Based on Information Value Theory

In recent years, online volunteers have played important roles in disaster response. After a majo... more In recent years, online volunteers have played important roles in disaster response. After a major disaster, hundreds of volunteers are often remotely convened by humanitarian organizations to map the affected area based on remote sensing images. Typically, the affected area is divided using a grid-based tessellation, and each volunteer can select one grid cell to start mapping. While this approach coordinates the efforts of volunteers, it does not differentiate the priorities of different cells. As a result, volunteers may map grid cells in a random order. Due to the spatial heterogeneity, different cells may contain geographic information that is of different value to emergency responders. Ideally, cells that potentially contain more valuable information should be assigned higher priority for mapping. This article presents an analytical framework for prioritizing the mapping of cells based on the values of information contained in these cells. Our objective is to provide guidance for online volunteers so that potentially more important cells are mapped first. We present a method that is based on information value theory and focus on road networks. We apply this method to a number of simulated scenarios and to a real disaster mapping case from the 2015 Nepal earthquake.

Development of three-dimensional urban scanning system using TerraExplorer Pro

Spatial signatures for geographic feature types: examining gazetteer ontologies using spatial statistics

Digital gazetteers play a key role in modern information systems and infrastructures. They facili... more Digital gazetteers play a key role in modern information systems and infrastructures. They facilitate (spatial) search, deliver contextual information to recommended systems, enrich textual information with geographical references, and provide stable identifiers to interlink actors, events, and objects by the places they interact with. Hence, it is unsurprising that gazetteers, such as GeoNames, are among the most densely interlinked hubs on the Web of Linked Data. A wide variety of digital gazetteers have been developed over the years to serve different communities and needs.

Task-oriented information value measurement based on space-time prisms

Recent years have witnessed a large increase in the amount of information available from the Web ... more Recent years have witnessed a large increase in the amount of information available from the Web and many other sources. Such an information deluge presents a challenge for individuals who have to identify useful information items to complete particular tasks in hand. Information value theory (IVT) from economics and artificial intelligence has provided some guidance on this issue. However, existing IVT studies often focus on monetary values, while ignoring the spatiotemporal properties which can play important roles in everyday tasks. In this paper, we propose a theoretical framework for task-oriented information value measurement. This framework integrates IVT with the space-time prism from time geography and measures the value of information based on its impact on an individual's space-time prisms and its capability of improving task planning. We develop and formalize this framework by extending the utility function from space-time accessibility studies and elaborate it using a simplified example from time geography. We conduct a simulation on a real-world transportation network using the proposed framework. Our research could be applied to improving information display on small-screen mobile devices (e.g., smartwatches) by assigning priorities to different information items.

Extracting and understanding urban areas of interest using geotagged photos

Urban areas of interest (AOI) refer to the regions within an urban environment that attract peopl... more Urban areas of interest (AOI) refer to the regions within an urban environment that attract people's attention. Such areas often have high exposure to the general public, and receive a large number of visits. As a result, urban AOI can reveal useful information for city planners, transportation analysts, and location-based service providers to plan new business, extend existing infrastructure, and so forth. Urban AOI exist in people's perception and are defined by behaviors. However, such perception was rarely captured until the Social Web information technology revolution. Social media data record the interactions between users and their surrounding environment, and thus have the potential to uncover interesting urban areas and their underlying spatiotemporal dynamics. This paper presents a coherent framework for extracting and understanding urban AOI based on geotagged photos. Six different cities from six different countries have been selected for this study, and Flickr photo data covering these cities in the past ten years (2004-2014) have been retrieved. We identify AOI using DBSCAN clustering algorithm, understand AOI by extracting distinctive textual tags and preferable photos, and discuss the spatiotemporal dynamics as well as some insights derived from the AOI. An interactive prototype has also been implemented as a proof-of-concept. While Flickr data have been used in this study, the presented framework can also be applied to other geotagged photos.

Estimating house vacancy rate in metropolitan areas using NPP-VIIRS nighttime light composite data

House vacancy rate (HVR) is an important index in assessing the healthiness of residential real e... more House vacancy rate (HVR) is an important index in assessing the healthiness of residential real estate market. Investigating HVR by field survey requires a lot of human and economic resources. The nighttime light (NTL) data, derived from Suomi National Polar-orbiting Partnership, can detect the artificial light from the Earth surface, and have been used to study social-economic activities. This paper proposes a method for estimating the HVR in metropolitan areas using NPP-VIIRS NTL composite data. This method combines NTL composite data with land cover information to extract the light intensity in urbanized areas. Then, we estimate the light intensity values for nonvacancy areas, and use such values to calculate the HVR in corresponding regions. Fifteen metropolitan areas in the United States have been selected for this study, and the estimated HVR values are validated using corresponding statistical data. The experimental results show a strong correlation between our derived HVR values and the statistical data. We also visualize the estimated HVR on maps, and discover that the spatial distribution of HVR is influenced by natural situations as well as the degree of urban development.

Metadata topic harmonization and semantic search for Linked-Data-driven geoportals: a case study using ArcGIS Online

Geoportals provide integrated access to geospatial resources, and enable both authorities and the... more Geoportals provide integrated access to geospatial resources, and enable both authorities and the general public to contribute and share data and services. An essential goal of geoportals is to facilitate the discovery of the available resources. Such a process relies heavily on the quality of metadata. While multiple metadata standards have been established, data contributers may adopt different standards when sharing their data via the same geoportal. This is especially the case for user-generated content where various terms and topics can be introduced to describe similar datasets. While this heterogeneity provides a wealth of perspectives, it also complicates resource discovery. With the fast development of the Semantic Web technologies, there is a rise of Linked-Data-driven portals. Although these novel portals open up new ways to organize metadata and retrieve resources, they lack effective semantic search methods. This article addresses the two challenges discussed above, namely the topic heterogeneity brought by multiple metadata standards and the lack of established semantic search in Linked-Data-driven geoportals. To harmonize the metadata topics, we employ a natural language processing method, namely Labeled Latent Dirichlet Allocation (LLDA), and train it using standardized metadata from Data.gov. With respect to semantic search, we construct thematic and geographic matching features from the textual metadata descriptions, and train a regression model via a human participants experiment. We evaluate our methods by examining their performances in addressing the two issues. Finally, we implement a semantics-enabled and Linked-Data-driven prototypical geoportal using a sample dataset from Esri's ArcGIS Online.

Modeling and mapping total freight traffic in China using NPP-VIIRS nighttime light composite data

In early 2013, the first global Suomi National Polar-orbiting Partnership (NPP) visible infrared ... more In early 2013, the first global Suomi National Polar-orbiting Partnership (NPP) visible infrared imaging radiometer suite (VIIRS) nighttime light composite data were released. Up to present, few studies have been conducted to evaluate the ability of NPP-VIIRS data to estimate the amount of freight traffic. This paper provides an exploratory evaluation on the NPP-VIIRS data for estimating the total freight traffic (TFT) in China, in comparison with the results derived from the Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) nighttime stable light composite data. We first corrected the original NPP-VIIRS data by employing a simple method to remove the outliers. The total nighttime light (TNL) which is measured by the sum value of all pixels from the nighttime light composite data was then regressed on TFT at the provincial level of China. Finally, the spatial distribution patterns of TFT were produced from the corrected NPP-VIIRS and DMSP-OLS data, respectively, and validated by the TFT statistics of 244 prefectures. The results have demonstrated that the corrected NPP-VIIRS data are more suitable for modeling TFT in China than the DMSP-OLS data.

Poverty evaluation using NPP-VIIRS nighttime light composite data at the county level in China

Poverty has appeared as one of the long-term predicaments facing development of human society dur... more Poverty has appeared as one of the long-term predicaments facing development of human society during the 21st century. Estimation of regional poverty level is a key issue for making strategies to eliminate poverty. This paper aims to evaluate the ability of the nighttime light composite data from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day-Night Band (DNB) carried by the Suomi National Polar-orbiting Partnership (NPP) Satellite in estimating poverty at the county level in China. Two major experiments are involved in this study, which include 1) 38 counties of Chongqing city and 2) 2856 counties of China. The first experiment takes Chongqing as an example and combines 10 socioeconomic variables into an integrated poverty index (IPI).

POI pulse: a multi-granular, semantic signatures-based information observatory for the interactive visualization of big geosocial data

The volume, velocity, and variety at which data are now becoming available allow us to study urba... more The volume, velocity, and variety at which data are now becoming available allow us to study urban environments based on human behavior at a spatial, temporal, and thematic granularity that was not achievable until now. Such data-driven approaches opens up additional, complementary perspectives on how urban systems function, especially if they are based on User-Generated Content (UGC). While the data sources, e.g., social media, introduce specific biases, they also open up new possibilities for scientists and the broader public. For instance, they provide answers to questions that previously could only be addressed by complex simulations or extensive human participant surveys. Unfortunately, many of the required datasets are locked in data silos that are only accessible via restricted APIs. Even if these data could be fully accessed, their naïve processing and visualization would surpass the abilities of modern computer architectures. Finally, the established place schemata used to study urban spaces differ substantially from UGC-based Point of Interest (POI) schemata. In this work, we present a multi-granular, datadriven, and theory-informed approach that addressed the key issues outlined above by introducing the theoretical and technical framework to interactively explore the pulse of a city based on social media.

Evaluating the ability of NPP-VIIRS nighttime light data to estimate the gross domestic product and the electric power consumption of China at multiple scales: a comparison with DMSP-OLS data

The nighttime light data records artificial light on the Earth's surface and can be used to estim... more The nighttime light data records artificial light on the Earth's surface and can be used to estimate the spatial distribution of the gross domestic product (GDP) and the electric power consumption (EPC). In early 2013, the first global NPP-VIIRS nighttime light data were released by the Earth Observation Group of National Oceanic and Atmospheric Administration's National Geophysical Data Center (NOAA/NGDC). As new-generation data, NPP-VIIRS data have a higher spatial resolution and a wider radiometric detection range than the traditional DMSP-OLS nighttime light data. This study aims to investigate the potential of NPP-VIIRS data in modeling GDP and EPC at multiple scales through a case study of China. A series of preprocessing procedures are proposed to reduce the background noise of original data and to generate corrected NPP-VIIRS nighttime light images. Subsequently, linear regression is used to fit the correlation between the total nighttime light (TNL) (which is extracted from corrected NPP-VIIRS data and DMSP-OLS data) and the

A multistage collaborative 3D GIS to support public participation

This paper presents a collaborative 3D GIS to support public participation. Realizing that public... more This paper presents a collaborative 3D GIS to support public participation. Realizing that public-involved decision-making is often a multistage process, the proposed system is designed to provide coherent support for collaborations in the different stages. We differentiate ubiquitous participation and intensive participation and identify their suitable application stages. The proposed system, then, supports both of the two types of participation by providing synchronous and asynchronous collaboration functionalities. Applying the concept of Digital Earth, the proposed system also features a virtual globe-based user interface. Such an interface integrates a variety of data, functions, and services into a unified virtual environment which is delivered to both experts and public participants through the Internet. The system has been designed as a general software framework and can be tailored for specific projects. In this study, we demonstrate it using a scene modeling case and provide a preliminary evaluation toward its usability.

Validation of total ozone column derived from OMPS using ground-based spectroradiometer measurements

Validation of total ozone column derived from OMPS using ground-based spectroradiometer measureme... more

A geospatial web portal for sharing and analyzing greenhouse gas data derived from satellite remote sensing images

Greenhouse gas data collected by different institutions throughout the world have significant sci... more Greenhouse gas data collected by different institutions throughout the world have significant scientific values for global climate change studies. Due to the diversity of data formats and different specifications of data access interfaces, most of those data should be first downloaded onto a local machine before they can be used. To overcome this limitation, we present a geospatial web portal for sharing and analyzing greenhouse gas data derived from remote sensing images. As a proof-ofconcept, a prototype has also been designed and implemented. The workflow of the web portal contains four processes: data access, data analysis, results visualization, and results output. A large volume of greenhouse gas data have been collected, described, and indexed in the portal, and a variety of data analysis services, such as calculating the temporal variation of regionally averaged column CO 2 values and analyzing the latitudinal variations of globally averaged column CO 2 values, are integrated into this portal. With the integrated geospatial data and services, researchers can collect and analyze greenhouse gas data online, and can preview and download the analysis results directly from the web portal. The geospatial web portal has been implemented as a web application, and we also used a study case to illustrate this framework.

Automated derivation of urban building density information using airborne LiDAR data and object-based method

Building density information is fundamentally important for urban design, planning and management... more Building density information is fundamentally important for urban design, planning and management and for urban environmental studies. This paper demonstrates that Building Coverage Ratio (BCR), Floor Area Ratio (FAR), and other building density indicators can be numerically and automatically derived from high-resolution airborne LiDAR data. An object-based method is proposed to process the LiDAR data for the building density information. The method consists of a sequence of numerical operations: generating the normalized Digital Surface Model (nDSM), extracting building objects, deriving object attributes, associating objects with the corresponding land lots, and computing building density indicators at land lot and urban district scales. The algorithms for these operations have been implemented as an ArcGIS extension module. The object-based method is applied to the processing of airborne LiDAR data over downtown Houston. Various attributes have been derived to quantify the building density, urban physical structure, and landscape morphological characteristics of the downtown area at three different spatial scales.