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Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
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A Geographic Information System (GIS) combines specialized software and hardware to effectively manage, analyze, and present spatial and related data. GIS software includes critical functionalities such as a user interface for easy navigation, database management tools for handling spatial and attribute data, and data retrieval features for efficient access. Analytical tools transform raw data into insights, while display functions produce maps and reports in various formats for effective...
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GPS surveying methods vary in application, accuracy, and data collection techniques, catering to diverse surveying and mapping needs. Static GPS, kinematic GPS, and real-time kinematic (RTK) surveying are widely used. Each technique offers distinct advantages.Static GPS involves placing one receiver at a known reference point and another at the target point. It collects exact positional data by observing multiple satellite ranges over an extended period, achieving centimeter-level accuracy for...
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Geographic Information Systems (GIS) operate across three levels of application, each representing an increasing degree of complexity: data management, analysis, and prediction. These levels reflect the expanding functionality and versatility of GIS technology in handling spatial data for diverse purposes.Data ManagementAt its foundational level, GIS serves as a tool for data management, enabling the input, storage, retrieval, and organization of spatial data. This level is often employed in...
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Trajectory Data Analyses for Pedestrian Space-time Activity Study
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Geo-temporal vehicular environmental sensing dataset.

Carmine Colarusso1, Marco Consales1, Gregorio Dalia1

  • 1University of Sannio, Department of Engineering, via Traiano 3, 82100, Benevento, Italy.

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|March 27, 2026
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Summary
This summary is machine-generated.

This dataset provides georeferenced, timestamped CO₂ and temperature measurements from mobile sensors in Benevento, Italy. It supports urban sensing, spatio-temporal analysis, and environmental monitoring research.

Keywords:
Air qualityPollutionSpatio-temporal dataUrban air monitoring

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Area of Science:

  • Environmental Science
  • Computer Science
  • Urban Planning

Background:

  • Urban environments exhibit complex spatio-temporal variability in air quality.
  • Mobile sensing platforms offer a method for high-resolution environmental data collection.
  • Existing datasets may lack fine-grained, multi-vehicle synchronized measurements.

Purpose of the Study:

  • To present a dataset of georeferenced, timestamped environmental measurements (CO₂, temperature) from a mobile sensing fleet.
  • To enable research in urban mobile sensing, spatio-temporal analysis, and environmental monitoring.
  • To facilitate the development of methods for robust, resource-aware urban monitoring.

Main Methods:

  • Utilized three vehicles equipped with low-power, vehicle-mounted sensor nodes.
  • Integrated Sensirion SCD41 CO₂ sensors and GPS for georeferencing and timestamping.
  • Collected continuous streams of CO₂, temperature, humidity, and speed data along diverse urban routes.

Main Results:

  • Generated a comprehensive dataset of synchronized, georeferenced environmental data from multiple mobile platforms.
  • Included raw sensor readings, spatial coordinates, temporal metadata, and vehicle identifiers.
  • Enabled multi-vehicle trajectory reconstruction and environment-driven analyses.

Conclusions:

  • The dataset supports research on urban mobile sensing, spatio-temporal analysis, and environmental monitoring.
  • Facilitates benchmarking of interpolation algorithms and validation of mobile sensing strategies.
  • Provides a resource for investigating sensor behavior in dynamic conditions and developing urban monitoring methods.