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Baidu Inc points of interest (poi) data
Points Of Interest (Poi) Data, supplied by Baidu Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/points+of+interest+(poi)+data/points+of+interest++pois+/pmc12265954-303-6-3
Average 90 stars, based on 1 article reviews
points of interest (poi) data - by Bioz Stars, 2026-11
90/100 stars

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Article Title: A systematic review on the associations between built environment and mental health among older people
Article Snippet: One study utilized Baidu’s Points of Interest (POI) data and data from 20 communities in Guangzhou, China, to explore the relationship between the BE and MH in the older adults.

Article Title: Research on the Interactive Relationship of Spatial Expansion between Estuarine and Coastal Port Cities
Article Snippet: Points of interest (POI) data were obtained from the Baidu map.

Article Title: Spatial syntax analysis on the renewal of the external space of mining heritage buildings —— A case study of Zollverein Coal Mine Industrial Complex
Article Snippet: The renovation of external spaces of mining heritage buildings should respect their historical and cultural value and be adapted to the specific characteristics of the mining area for spatial restructuring and functional replacement.. Current research often relies on subjective experience and focuses on qualitative analysis, lacking in-depth quantitative analysis.. Building upon the summary of research on mining heritage exterior spaces and spatial syntax studies, this paper proposes a research methodology that quantitatively analyzes the accessibility and integration of mining heritage exterior spaces using spatial syntax convex space and axis map models.

Article Title: A method to evaluate task-specific importance of spatio-temporal units based on explainable artificial intelligence
Article Snippet: Big geo-data are often aggregated according to spatio-temporal units for analyzing human activities and urban environments.. Many applications categorize such data into groups and compare the characteristics across groups.. The intergroup differences vary with spatio-temporal units, and the essential is to identify the spatiotemporal units with apparently different data characteristics.

Article Title: Demand Prediction and Influencing Factors Analysis of Shared Bicycles near Public Transportation Stations based on GCN
Article Snippet: Traffic prediction is a pivotal technology in the development of intelligent transportation systems.. Real-time and accurate traffic prediction holds significance in route planning, resource allocation, and improving travel efficiency.. To enhance the convenience of shared bicycle travel near public transportation hubs and optimize shared bicycle deployment strategies, this paper proposes the utilization of a graph convolutional neural network prediction model.

Article Title: Constructing a Flood-Adaptive Ecological Security Pattern from the Perspective of Ecological Resilience: A Case Study of the Main Urban Area in Wuhan.
Article Snippet: Public Health 2023, 20, 385 4 of 19 from the Wuhan Municipal Water Affairs Authority (http://swj.wuhan.gov.cn/tzdt/jcss/ 202005/t20200511_1309387.html, accessed on 25 December 2021), the net primary productivity (NPP) data of vegetation in the main urban area of Wuhan with 30 M accuracy comes from the Resource and Environmental Science and Data Center of the Chinese Academy of Sciences (https://www.resdc.cn/, accessed on 10 March 2022), the data of points of interest (POI) in the main urban area of Wuhan are from Baidu Map Open Platform (https://lbsyun.baidu.com/, accessed on 25 December 2021), and the rest of the meteorological data in Wuhan comes from the “Wuhan Statistical Yearbook” (2010–2020) (http://tjj.wuhan.gov.cn/tjfw/tjnj/, accessed on 18 November 2021).



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Datasets used in the study area.
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Examples of some tourist attractions in <t> Shaoxing. </t>
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Image Search Results


Datasets used in the study area.

Journal: PLOS One

Article Title: A population spatialization method based on the integration of feature selection and an improved random forest model

doi: 10.1371/journal.pone.0321263

Figure Lengend Snippet: Datasets used in the study area.

Article Snippet: Point-of-interest data (POI) , Baidu map , The data include 14 points of interest as follows: leisure and entertainment, financial services, education and training, life services, government agencies, medical care, automobile services, companies and enterprises, transportation facilities, shopping, accommodation, tourist attractions, catering, and real estate..

Techniques: Plasmid Preparation

Examples of some tourist attractions in  Shaoxing.

Journal: PLOS ONE

Article Title: Spatial distribution characteristics and influencing factors of tourism resources based on point of interest data

doi: 10.1371/journal.pone.0310487

Figure Lengend Snippet: Examples of some tourist attractions in Shaoxing.

Article Snippet: This paper obtains the data of points of interest (POI) in Shaoxing City from Autonavi Open Data Platform as the research object, and adopts the ArcGIS10.2 software and spatial statistical analysis methods such as average nearest neighbor analysis, nuclear density analysis, imbalance index analysis and geographic detector to conduct statistical analysis on the spatial pattern of scenic spots in Shaoxing City and its sub-districts (counties and cities).

Techniques:

Spatial visualization distribution map of tourist attractions in Shaoxing.

Journal: PLOS ONE

Article Title: Spatial distribution characteristics and influencing factors of tourism resources based on point of interest data

doi: 10.1371/journal.pone.0310487

Figure Lengend Snippet: Spatial visualization distribution map of tourist attractions in Shaoxing.

Article Snippet: This paper obtains the data of points of interest (POI) in Shaoxing City from Autonavi Open Data Platform as the research object, and adopts the ArcGIS10.2 software and spatial statistical analysis methods such as average nearest neighbor analysis, nuclear density analysis, imbalance index analysis and geographic detector to conduct statistical analysis on the spatial pattern of scenic spots in Shaoxing City and its sub-districts (counties and cities).

Techniques:

Nearest neighbor ratio and spatial status distribution of districts (counties and cities) in  Shaoxing.

Journal: PLOS ONE

Article Title: Spatial distribution characteristics and influencing factors of tourism resources based on point of interest data

doi: 10.1371/journal.pone.0310487

Figure Lengend Snippet: Nearest neighbor ratio and spatial status distribution of districts (counties and cities) in Shaoxing.

Article Snippet: This paper obtains the data of points of interest (POI) in Shaoxing City from Autonavi Open Data Platform as the research object, and adopts the ArcGIS10.2 software and spatial statistical analysis methods such as average nearest neighbor analysis, nuclear density analysis, imbalance index analysis and geographic detector to conduct statistical analysis on the spatial pattern of scenic spots in Shaoxing City and its sub-districts (counties and cities).

Techniques:

Kernel density map of spatial distribution of scenic spots in Shaoxing.

Journal: PLOS ONE

Article Title: Spatial distribution characteristics and influencing factors of tourism resources based on point of interest data

doi: 10.1371/journal.pone.0310487

Figure Lengend Snippet: Kernel density map of spatial distribution of scenic spots in Shaoxing.

Article Snippet: This paper obtains the data of points of interest (POI) in Shaoxing City from Autonavi Open Data Platform as the research object, and adopts the ArcGIS10.2 software and spatial statistical analysis methods such as average nearest neighbor analysis, nuclear density analysis, imbalance index analysis and geographic detector to conduct statistical analysis on the spatial pattern of scenic spots in Shaoxing City and its sub-districts (counties and cities).

Techniques:

Statistics of scenic spots in districts (county, city) in  Shaoxing.

Journal: PLOS ONE

Article Title: Spatial distribution characteristics and influencing factors of tourism resources based on point of interest data

doi: 10.1371/journal.pone.0310487

Figure Lengend Snippet: Statistics of scenic spots in districts (county, city) in Shaoxing.

Article Snippet: This paper obtains the data of points of interest (POI) in Shaoxing City from Autonavi Open Data Platform as the research object, and adopts the ArcGIS10.2 software and spatial statistical analysis methods such as average nearest neighbor analysis, nuclear density analysis, imbalance index analysis and geographic detector to conduct statistical analysis on the spatial pattern of scenic spots in Shaoxing City and its sub-districts (counties and cities).

Techniques:

Lorenz curve of scenic spot distribution in Shaoxing.

Journal: PLOS ONE

Article Title: Spatial distribution characteristics and influencing factors of tourism resources based on point of interest data

doi: 10.1371/journal.pone.0310487

Figure Lengend Snippet: Lorenz curve of scenic spot distribution in Shaoxing.

Article Snippet: This paper obtains the data of points of interest (POI) in Shaoxing City from Autonavi Open Data Platform as the research object, and adopts the ArcGIS10.2 software and spatial statistical analysis methods such as average nearest neighbor analysis, nuclear density analysis, imbalance index analysis and geographic detector to conduct statistical analysis on the spatial pattern of scenic spots in Shaoxing City and its sub-districts (counties and cities).

Techniques:

Standard deviation ellipse in districts (counties and cities) in Shaoxing.

Journal: PLOS ONE

Article Title: Spatial distribution characteristics and influencing factors of tourism resources based on point of interest data

doi: 10.1371/journal.pone.0310487

Figure Lengend Snippet: Standard deviation ellipse in districts (counties and cities) in Shaoxing.

Article Snippet: This paper obtains the data of points of interest (POI) in Shaoxing City from Autonavi Open Data Platform as the research object, and adopts the ArcGIS10.2 software and spatial statistical analysis methods such as average nearest neighbor analysis, nuclear density analysis, imbalance index analysis and geographic detector to conduct statistical analysis on the spatial pattern of scenic spots in Shaoxing City and its sub-districts (counties and cities).

Techniques: Standard Deviation

Standard deviation ellipse parameters in districts (counties and cities) in  Shaoxing.

Journal: PLOS ONE

Article Title: Spatial distribution characteristics and influencing factors of tourism resources based on point of interest data

doi: 10.1371/journal.pone.0310487

Figure Lengend Snippet: Standard deviation ellipse parameters in districts (counties and cities) in Shaoxing.

Article Snippet: This paper obtains the data of points of interest (POI) in Shaoxing City from Autonavi Open Data Platform as the research object, and adopts the ArcGIS10.2 software and spatial statistical analysis methods such as average nearest neighbor analysis, nuclear density analysis, imbalance index analysis and geographic detector to conduct statistical analysis on the spatial pattern of scenic spots in Shaoxing City and its sub-districts (counties and cities).

Techniques: Standard Deviation

Factors affecting the spatial distribution of tourist attractions.

Journal: PLOS ONE

Article Title: Spatial distribution characteristics and influencing factors of tourism resources based on point of interest data

doi: 10.1371/journal.pone.0310487

Figure Lengend Snippet: Factors affecting the spatial distribution of tourist attractions.

Article Snippet: This paper obtains the data of points of interest (POI) in Shaoxing City from Autonavi Open Data Platform as the research object, and adopts the ArcGIS10.2 software and spatial statistical analysis methods such as average nearest neighbor analysis, nuclear density analysis, imbalance index analysis and geographic detector to conduct statistical analysis on the spatial pattern of scenic spots in Shaoxing City and its sub-districts (counties and cities).

Techniques: