DOI: 10.5281/zenodo.14063683
title: Spatially Gridded Vehicle Count Analysis for Connaught Place, New Delhi

description: Our Gridded Vehicle Count Analysis for Connaught Place, New Delhi, is a detailed geospatial analysis focusing on vehicular emissions in a highly trafficked urban area. Here's an overview:

Objective

The analysis aims to quantify vehicle density in Connaught Place, a major commercial and tourist hub, by generating a spatially gridded vehicle count dataset. By doing this, the project provides insights into traffic patterns and potential emission hotspots, supporting air quality forecasts and urban planning.

Data Collection



Satellite Imagery: You collected high-resolution WorldView satellite imagery, zoomed specifically over Connaught Place to ensure the detail needed for vehicle detection.

Image Preprocessing: Using PyQGIS, you loaded the satellite imagery, created a 0.09-degree buffer around Connaught Place, and generated a grid layer with 150m x 150m cells to spatially segment the area for analysis.

Geotagged .tiff Files: The images were exported as geotagged .tiff files, preserving spatial information for each grid cell, facilitating precise location-based analysis.


Detection and Analysis



Object Detection Models: You employed YOLOv8 through YOLOv10 and other state-of-the-art deep learning models to detect various vehicle types, such as cars, buses, and trucks. This approach helped accurately identify vehicle counts in real time.

Class-wise Detection: Specific object classes were defined, allowing for detailed counts by vehicle type, which is crucial for emission factor calculations.

Result Export: The detected vehicle counts and corresponding latitude/longitude data were stored in netCDF files, enabling the creation of gridded emission inventories.


Outcomes and Applications

The gridded dataset enables real-time monitoring of vehicular density and emissions over time. This data:



Enhances air quality models by integrating localized emission sources.

Assists policymakers in targeting emission reduction initiatives.

Provides a foundational layer for studies on urban traffic flow and its environmental impacts in high-density zones like Connaught Place.


In summary, this analysis provides a scientifically robust and spatially precise view of vehicular density, supporting both atmospheric studies and practical urban management decisions.
description: The cat_counts_updated.nc file is a netCDF (.nc) file that contains spatially gridded data on vehicle and miscellaneous object counts over Connaught Place, New Delhi, across multiple time points. This file structure facilitates efficient storage, retrieval, and analysis of vehicle data. Here’s a breakdown of its structure and key attributes:

File Structure Overview



Dimensions: The file has two main dimensions:



time: UNLIMITED dimension, allowing data to be added over time. Currently, there are 7 time points.

records: This dimension represents spatial grid cells, with 2929 records for each time point.



Variables: The file stores several key variables:



time: Represents the time of data capture. It includes metadata specifying units (day as %Y%m%d.%f) and follows the proleptic Gregorian calendar. This variable enables a chronological sequence of vehicle detections.

lat and lon: These variables store the latitude and longitude of each grid cell at each time point. They are stored as float and provide geospatial coordinates for each vehicle count, making the data spatially explicit.

cars, buses, brick_kilns, and miscellaneous: Each of these is an integer variable that represents the count of a specific object type per grid cell at each time point.



cars: Count of cars detected per grid cell.

buses: Count of buses detected per grid cell.

brick_kilns: Count of brick kilns detected per grid cell.

miscellaneous: Count of miscellaneous objects (other than the above categories) detected per grid cell.






Global Attributes



CDI: Specifies the Climate Data Interface (CDI) version used for handling this file (2.4.0), linking to its documentation for reference.

Conventions: The file adheres to the CF (Climate and Forecast) metadata conventions, specifically CF-1.6, which standardizes the representation of spatial and temporal metadata, enhancing interoperability with climate and weather datasets.

history: Logs the creation history, detailing that the file was created by concatenating data from seven different input files (detections_1.nc to detections_7.nc), allowing traceability and reproducibility.

CDO: Identifies that the Climate Data Operators (CDO) tool version 2.4.0 was used, indicating the processing environment.


Data Sample (Values for time)



The time variable currently holds placeholder values of 0, which implies that further specification (e.g., timestamps for each detection) may be needed to fully represent the temporal aspects.


Applications and Uses

This .nc file provides a compact, structured format ideal for:



Air Quality Modeling: Linking vehicle counts with emissions to model air pollution in an urban setting.

Temporal Analysis: Tracking changes in vehicle density and other object counts over time, essential for time-based analyses such as peak traffic hours or daily variations.

Spatial Analysis: With latitude and longitude data for each count, this file enables GIS-based visualizations, helping to pinpoint areas with high vehicle density.


In summary, cat_counts_updated.nc serves as a comprehensive dataset for spatially and temporally resolved vehicle count analysis, optimized for efficient retrieval and integration into atmospheric and urban studies.