Case Study On Parking Space Analysis Using OpenStreetMap Data


Parking Space Analysis is a critical facet of urban planning and transportation management, aiming to ensure convenient access to parking facilities for city residents and visitors. In this case study, we delve into the evaluation of parking space distribution and availability within a city to gain a comprehensive understanding of areas grappling with parking shortages. We will employ geospatial data from OpenStreetMap, primarily in the form of GeoJSON, encompassing data pertaining to both street parking and dedicated parking lots. This data will be harnessed using the Overpass Turbo tool, a powerful resource for extracting location-based information from OpenStreetMap.

Theme: Parking Space Analysis

Parking is an essential component of urban infrastructure, impacting not only the daily lives of city dwellers but also broader issues such as traffic congestion, environmental sustainability, and the vitality of local businesses. As cities grow and evolve, understanding the distribution and availability of parking spaces becomes pivotal to enhancing urban mobility, reducing congestion, and ensuring equitable access to urban resources.

Problem Statement

The overarching problem this case study aims to address is the pressing need to evaluate the distribution and availability of parking spaces within a city. The specific objectives include:

  • Identifying areas within the city where there is an evident shortage of parking spaces, leading to congestion and inconvenience.
  • Analyzing the types of parking spaces available, differentiating between street parking and dedicated parking lots.
  • Investigating the geographical distribution of parking spaces, with a focus on identifying areas with a high demand for parking.
  • Assessing the potential impact of parking shortages on local businesses, residents, and city traffic flow.

By addressing these objectives, we aim to provide actionable insights and data-driven recommendations for city planners, policymakers, and transportation authorities. These insights can inform strategies to improve the allocation of parking resources, reduce urban congestion, and enhance the overall quality of life in the city.

Data Source (OpenStreetMap)

OpenStreetMap (OSM) is a collaborative platform that provides comprehensive geospatial data contributed by a global community of mappers. It offers a wealth of information about various geographical features, including parking spaces, streets, and amenities. For this case study, we will leverage OSM’s wealth of parking-related data, encompassing both on-street parking and dedicated parking lots. We will use GeoJSON, which are versatile format for geographic data representation, and extract this data using Overpass Turbo, a specialized tool that allows us to access, filter, and download specific subsets of OSM data relevant to our analysis.

With this robust dataset and a data-driven analytical approach, we aim to shed light on the complex dynamics of parking availability in the city and contribute to informed decision-making in the realm of urban planning and transportation management.

Data Collection

Acquiring accurate and up-to-date parking-related data from OpenStreetMap (OSM) is a crucial step in this case study. To extract the dataset, we will utilize the Overpass Turbo tool, which allows us to query and retrieve specific geographic information. Here are the steps to obtain the dataset:

  1. Access Overpass Turbo
    Visit the Overpass Turbo web application
  2. Define Your Query
    In the Overpass Turbo interface, you’ll see a text editor on the left side. This is where you will write your query to specify the data you want to extract. You can use the Overpass Query Language (Overpass QL) to create your query.
  3. Write Your Query
    To extract parking-related data, you can use Overpass QL queries like the following example

    overpass_query = """
    out body;
    out skel qt;

    This query fetches all nodes, ways, and relations tagged with “amenity=parking” from OSM data. You can customize your query further to narrow down the data based on your requirements.

  4. Run the Query
    After writing your query, click the “Run” button on the Overpass Turbo interface. This will execute your query and display the results on the map on the right side of the screen.
  5. Download the Data
    Once you are satisfied with the query results, you can export the data in various formats, including GeoJSON. Click on the “Export” button, and select the format you prefer (e.g., GeoJSON). This will initiate the download of the data.
  6. Save the Data and Record the Date
    Save the downloaded dataset to your local storage. Additionally, it’s essential to record the date of data extraction as it may impact the accuracy of your analysis. OSM data is continually updated by contributors, so it’s crucial to be aware of the dataset’s freshness.
  7. Document the Query
    It’s advisable to document the specific query used for data extraction, as well as any filters or criteria applied. This documentation will help ensure the transparency and reproducibility of your analysis.

By following these steps, you can obtain the necessary parking-related data from OpenStreetMap using Overpass Turbo. It’s important to note that periodically refreshing the dataset may be necessary to maintain the accuracy and relevance of your analysis, especially in dynamic urban environments where parking infrastructure can change over time.

Data Preprocessing

Data preprocessing is a crucial step to ensure that the dataset obtained from Overpass Turbo is clean, well-organized, and suitable for analysis. Here’s how you can perform data preprocessing in Google Colab:

1. Data Cleaning

Start by examining the retrieved GeoJSON data to identify any inconsistencies, missing values, or inaccuracies. Common cleaning tasks may include:

  • Removing duplicate entries if any exist.
  • Handling missing or invalid geographic coordinates.
  • Eliminating outliers or erroneous data points.
  • You can use Python libraries like `pandas` or `geopandas` to load and manipulate the data. For example, to load a GeoJSON file into a `geopandas` dataframe, you can use
import geopandas as gpd

# Load the GeoJSON data into a GeoDataFrame
parking_data = gpd.read_file("parking_data.geojson")

2. Data Filtering

Depending on your specific analysis goals, you may need to filter the data to focus on relevant features. Filtering criteria could include:

  • Selecting only specific types of parking (e.g., street parking or parking lots) based on the “amenity” tag.
  • Restricting the dataset to a particular geographic area of interest.
  • You can apply filters using `geopandas` or standard Python operations.

3. Data Organization

  • To make your analysis more efficient, you can organize the data by creating subsets or grouping features based on attributes. For instance, you can group parking data by type, location, or other relevant categories.
  • Use Python’s `pandas` or `geopandas` capabilities to create subsets and group data as needed.

4. Data Transformation

Depending on the analysis you plan to perform, you may need to transform the dataset in various ways. Common data transformations include:

  • Converting geographic coordinates from one coordinate reference system (CRS) to another, if necessary.
  • Calculating new attributes or derived data, such as distance to key locations or density of parking spaces.
  • Aggregating data to a coarser spatial resolution if the initial data is too detailed.

Use libraries like `geopandas` and `shapely` for spatial operations and calculations. For example, you can project the data to a different CRS:

# Project to a new coordinate reference system (CRS)
parking_data = parking_data.to_crs("EPSG:4326")

Remember to thoroughly document each step of the data preprocessing process to ensure transparency and reproducibility of your analysis. The cleaned and processed dataset will serve as the foundation for your parking space analysis, making it more accurate and informative for your research.


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