Publisher
University Libraries, Virginia TechLocation
New Delhi, India
Mumbai, India
Bengaluru, India
Chennai, India
Kolkata, IndiaCorresponding Author Name
Nitheshnirmal SadhasivamFiles/Folders in Dataset and Description
The data contains 11 files:
1. New_Delhi_VLM_data.csv contains the vertical land motion (VLM) in IGS14 for New Delhi, India. The file has three columns: longitude (deg.), latitude (deg.), vlm(cm/yr).
2. Mumbai_VLM_data.csv contains the vertical land motion (VLM) in IGS14 for Mumbai, India. The file has three columns: longitude (deg.), latitude (deg.), vlm(cm/yr).
3. Bengaluru_VLM_data.csv contains the vertical land motion (VLM) in IGS14 for Bengaluru, India. The file has three columns: longitude (deg.), latitude (deg.), vlm(cm/yr).
4. Chennai_VLM_data.csv contains the vertical land motion (VLM) in IGS14 for Chennai, India. The file has three columns: longitude (deg.), latitude (deg.), vlm(cm/yr).
5. Kolkata_VLM_data.csv contains the vertical land motion (VLM) in IGS14 for Kolkata, India. The file has three columns: longitude (deg.), latitude (deg.), vlm(cm/yr).
6. The file New_Delhi_Buildings_Damage_Risk.zip contains a multi-polygon shapefile representing buildings in New Delhi, each with damage risk assessments across different scenarios. The shapefile includes four fields—Risk_InSAR, Risk_10yrs, Risk_30yrs, and Risk_50yrs—which indicate the building damage risk during the InSAR observation period, and projected over 10, 30, and 50 years, respectively. Each field categorizes risk into five levels: 0 (Very Low), 1 (Low), 2 (Medium), 3 (High), and 4 (Very High), with NaN values where no data is available. The shapefile can be opened and visualized in any GIS software, such as ArcGIS or QGIS, or processed programmatically using tools like Python, R or Matlab.
7. The file Mumbai_Buildings_Damage_Risk.zip contains a multi-polygon shapefile representing buildings in Mumbai, each with damage risk assessments across different scenarios. The shapefile includes four fields—Risk_InSAR, Risk_10yrs, Risk_30yrs, and Risk_50yrs—which indicate the building damage risk during the InSAR observation period, and projected over 10, 30, and 50 years, respectively. Each field categorizes risk into five levels: 0 (Very Low), 1 (Low), 2 (Medium), 3 (High), and 4 (Very High), with NaN values where no data is available. The shapefile can be opened and visualized in any GIS software, such as ArcGIS or QGIS, or processed programmatically using tools like Python, R or Matlab.
8. The file Bengaluru_Buildings_Damage_Risk.zip contains a multi-polygon shapefile representing buildings in Bengaluru, each with damage risk assessments across different scenarios. The shapefile includes four fields—Risk_InSAR, Risk_10yrs, Risk_30yrs, and Risk_50yrs—which indicate the building damage risk during the InSAR observation period, and projected over 10, 30, and 50 years, respectively. Each field categorizes risk into five levels: 0 (Very Low), 1 (Low), 2 (Medium), 3 (High), and 4 (Very High), with NaN values where no data is available. The shapefile can be opened and visualized in any GIS software, such as ArcGIS or QGIS, or processed programmatically using tools like Python, R or Matlab.
9. The file Chennai_Buildings_Damage_Risk.zip contains a multi-polygon shapefile representing buildings in Chennai, each with damage risk assessments across different scenarios. The shapefile includes four fields—Risk_InSAR, Risk_10yrs, Risk_30yrs, and Risk_50yrs—which indicate the building damage risk during the InSAR observation period, and projected over 10, 30, and 50 years, respectively. Each field categorizes risk into five levels: 0 (Very Low), 1 (Low), 2 (Medium), 3 (High), and 4 (Very High), with NaN values where no data is available. The shapefile can be opened and visualized in any GIS software, such as ArcGIS or QGIS, or processed programmatically using tools like Python, R or Matlab.
10. The file Kolkata_Buildings_Damage_Risk.zip contains a multi-polygon shapefile representing buildings in Kolkata, each with damage risk assessments across different scenarios. The shapefile includes four fields—Risk_InSAR, Risk_10yrs, Risk_30yrs, and Risk_50yrs—which indicate the building damage risk during the InSAR observation period, and projected over 10, 30, and 50 years, respectively. Each field categorizes risk into five levels: 0 (Very Low), 1 (Low), 2 (Medium), 3 (High), and 4 (Very High), with NaN values where no data is available. The shapefile can be opened and visualized in any GIS software, such as ArcGIS or QGIS, or processed programmatically using tools like Python, R or Matlab.
11. Building_Collapse_Incidents.xlsx contains the catalog of 26 building collapse data collected for this study. The file has 8 columns: Column 1: Date, Column 2: Address, Column 3: Structure that collapsed, Column 4: City, Column 5: Injury, Column 6: Death, Column 7: Research paper, Column 8: Cited sources.
