Sensen Wu
Biographic Data
| ID | 7544685 |
|---|---|
| NAME | Sensen Wu |
| GIVEN NAMES | Sensen |
| FAMILY NAME | Wu |
| SIGNATURE | WU S |
| AFFILIATIONS | Zhejiang University |
| ORCID | 0000-0002-2476-8087 |
| VERIFIED | Yes |
| TOTAL WORKS | 11 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 11 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2021 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Risk prediction model of cognitive performance in older people with cardiovascular diseases
Background and aim: Changes in cognitive function are commonly associated with aging in patients with cardiovascular diseases. The objective of this research was to construct and validate a nomogram-based predictive model for the identification of cognitive impairment in older people suffering from cardiovascular diseases. Methods and results: This retrospective study included 498 participants with cardiovascular diseases aged >60 selected from t…
A Network Approach for Discovering Spatially Associated Objects
Discovering spatially associated objects involves measuring objects’ similarities and retrieving associated objects. The integration of spatial topology and network models for discovering associated objects remains largely unexplored. Here, the concept of a maximum topological accessibility path was developed to quantify objects’ similarity attenuation. Considering the topological accessibility and spatial feature similarity of network nodes, an …
An LLM-Based Inventory Construction Framework of Urban Ground Collapse Events with Spatiotemporal Locations
Historical news media reports serve as a vital data source for understanding the risk of urban ground collapse (UGC) events. At present, the application of large language models (LLMs) offers unprecedented opportunities to effectively extract UGC events and their spatiotemporal information from a vast amount of news reports and media data. Therefore, this study proposes an LLM-based inventory construction framework consisting of three steps: news…
Quantitative Study on American Covid-19 Epidemic Predictions and Scenario Simulations
The COVID-19 pandemic has had a profound impact on people’s lives, making accurate prediction of epidemic trends a central focus in COVID-19 research. This study innovatively utilizes a spatiotemporal heterogeneity analysis (GTNNWR) model to predict COVID-19 deaths, simulate pandemic prevention scenarios, and quantitatively assess their preventive effects. The results show that the GTNNWR model exhibits superior predictive capacity to the convent…
DCPMS
Raster data represent one of the fundamental data formats utilized in GIS. As the technology used to observe the Earth continues to evolve, the spatial and temporal resolution of raster data is becoming increasingly refined, while the data scale is expanding. One of the key issues in the development of GIS technology is to determine how to make large-scale raster data better to provide computation, visualization, and analysis services in the Inte…
A deep multi-scale neural networks for crime hotspot mapping prediction
Assessment of coordinated development of digital technology and cultural industries in sustainable environment
This article proposes an analytical framework for evaluating digital technology and cultural industries in a sustainable environment, employing the entropy weight method and the coordinated development model to measure the level of coordinated development of digital technology and cultural industries in each province in the Chinese mainland. The results show that most provinces in mainland China are in the early stage of coordinated development, …
House Price Valuation Model Based on Geographically Neural Network Weighted Regression
Confronted with the spatial heterogeneity of the real estate market, some traditional research has utilized geographically weighted regression (GWR) to estimate house prices. However, its predictive power still has some room to improve, and its kernel function is limited in some simple forms. Therefore, we propose a novel house price valuation model, which is combined with geographically neural network weighted regression (GNNWR) to improve the a…
Effects of Climate Change on Corn Yields
Food security has been one of the greatest global concerns facing the current complicated situation. Among these, the impact of climate change on agricultural production is dynamic over time and space, making it a major challenge to food security. Taking the U.S. Corn Belt as an example, we introduce a geographically and temporally weighted regression (GTWR) model that can handle both temporal and spatial non-stationarity in the relationship betw…
A GloVe-Based POI Type Embedding Model for Extracting and Identifying Urban Functional Regions
Points-of-interest (POIs) are an important carriers of location text information in smart cities and have been widely used to extract and identify urban functional regions. However, it is difficult to model the relationship between POIs and urban functional types using existing methods due to insufficient POIs information mining. In this study, we propose a Global Vectors (GloVe)-based, POI type embedding model (GPTEM) to extract and identify urb…
Using Geographically Weighted Regression to Study the Seasonal Influence of Potential Risk Factors on the Incidence of HFMD on the Chinese Mainland
Hand, foot, and mouth disease (HFMD) is an epidemic infectious disease in China. Its incidence is affected by a variety of natural environmental and socioeconomic factors, and its transmission has strong seasonal and spatial heterogeneity. To quantify the spatial relationship between the incidence of HFMD (I-HFMD) and eight potential risk factors (temperature, humidity, precipitation, wind speed, air pressure, altitude, child population density, …
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A GloVe-Based POI Type Embedding Model for Extracting and Identifying Urban Functional Regions
Points-of-interest (POIs) are an important carriers of location text information in smart cities and have been widely used to extract and identify urban functional regions. However, it is difficult to model the relationship between POIs and urban functional types using existing methods due to insufficient POIs information mining. In this study, we propose a Global Vectors (GloVe)-based, POI type embedding model (GPTEM) to extract and identify urb…
Using Geographically Weighted Regression to Study the Seasonal Influence of Potential Risk Factors on the Incidence of HFMD on the Chinese Mainland
Hand, foot, and mouth disease (HFMD) is an epidemic infectious disease in China. Its incidence is affected by a variety of natural environmental and socioeconomic factors, and its transmission has strong seasonal and spatial heterogeneity. To quantify the spatial relationship between the incidence of HFMD (I-HFMD) and eight potential risk factors (temperature, humidity, precipitation, wind speed, air pressure, altitude, child population density, …
House Price Valuation Model Based on Geographically Neural Network Weighted Regression
Confronted with the spatial heterogeneity of the real estate market, some traditional research has utilized geographically weighted regression (GWR) to estimate house prices. However, its predictive power still has some room to improve, and its kernel function is limited in some simple forms. Therefore, we propose a novel house price valuation model, which is combined with geographically neural network weighted regression (GNNWR) to improve the a…
Effects of Climate Change on Corn Yields
Food security has been one of the greatest global concerns facing the current complicated situation. Among these, the impact of climate change on agricultural production is dynamic over time and space, making it a major challenge to food security. Taking the U.S. Corn Belt as an example, we introduce a geographically and temporally weighted regression (GTWR) model that can handle both temporal and spatial non-stationarity in the relationship betw…
Assessment of coordinated development of digital technology and cultural industries in sustainable environment
This article proposes an analytical framework for evaluating digital technology and cultural industries in a sustainable environment, employing the entropy weight method and the coordinated development model to measure the level of coordinated development of digital technology and cultural industries in each province in the Chinese mainland. The results show that most provinces in mainland China are in the early stage of coordinated development, …
An LLM-Based Inventory Construction Framework of Urban Ground Collapse Events with Spatiotemporal Locations
Historical news media reports serve as a vital data source for understanding the risk of urban ground collapse (UGC) events. At present, the application of large language models (LLMs) offers unprecedented opportunities to effectively extract UGC events and their spatiotemporal information from a vast amount of news reports and media data. Therefore, this study proposes an LLM-based inventory construction framework consisting of three steps: news…
Quantitative Study on American Covid-19 Epidemic Predictions and Scenario Simulations
The COVID-19 pandemic has had a profound impact on people’s lives, making accurate prediction of epidemic trends a central focus in COVID-19 research. This study innovatively utilizes a spatiotemporal heterogeneity analysis (GTNNWR) model to predict COVID-19 deaths, simulate pandemic prevention scenarios, and quantitatively assess their preventive effects. The results show that the GTNNWR model exhibits superior predictive capacity to the convent…
DCPMS
Raster data represent one of the fundamental data formats utilized in GIS. As the technology used to observe the Earth continues to evolve, the spatial and temporal resolution of raster data is becoming increasingly refined, while the data scale is expanding. One of the key issues in the development of GIS technology is to determine how to make large-scale raster data better to provide computation, visualization, and analysis services in the Inte…
A deep multi-scale neural networks for crime hotspot mapping prediction
Risk prediction model of cognitive performance in older people with cardiovascular diseases
Background and aim: Changes in cognitive function are commonly associated with aging in patients with cardiovascular diseases. The objective of this research was to construct and validate a nomogram-based predictive model for the identification of cognitive impairment in older people suffering from cardiovascular diseases. Methods and results: This retrospective study included 498 participants with cardiovascular diseases aged >60 selected from t…
A Network Approach for Discovering Spatially Associated Objects
Discovering spatially associated objects involves measuring objects’ similarities and retrieving associated objects. The integration of spatial topology and network models for discovering associated objects remains largely unexplored. Here, the concept of a maximum topological accessibility path was developed to quantify objects’ similarity attenuation. Considering the topological accessibility and spatial feature similarity of network nodes, an …
Computer Science (8 works) · Geography (8 works) · Artificial Intelligence (4 works) · Environmental health (3 works) · Geographic Information Systems Studies (3 works) · Mathematics (3 works) · Medicine (3 works) · Population (3 works) · Regression analysis (3 works) · Artificial neural network (2 works)