Creating Livable Spaces: Introducing the Walkability Index for Sustainable Mobility
Walkability is increasingly being recognized as essential element of healthier, more sustainable, and more livable communities. Given that the transport sector is one of the largest contributors to CO₂ emissions, and—with limited progress in reducing these emissions—promoting sustainable mobility has become an urgent priority. Walking and cycling are among the most efficient, low-impact modes of urban transportation, making their improvement essential for creating healthier, more livable cities. Furthermore, walkability is an important component of the “15-minute city” model, where essential services and amenities are accessible within a short walk from any point in the city.
What is Walkability?
Walkability refers to how friendly an area is to walking, influenced by factors such as pedestrian pathways, street safety, proximity of amenities (like benches or cafés), and the overall comfort and attractiveness of the walking environment. A walkable city encourages residents to choose walking over commuting by car, regardless of the season or time of day. This shift reduces carbon footprints, improves energy efficiency, and benefits public health. Walkability also brings economic and social advantages, such as increased business activity in walkable neighborhoods and active social interaction within the community.
Our Approach to Walkability
At the core of climate-friendly urban design is the need for practical, data-driven indicators that guide effective local action. HeiGIT, the Heidelberg Institute for Geoinformation Technology, addresses this need by developing indicators to support sustainable mobility and other urban climate action initiatives. These indicators help local stakeholders make informed decisions by visualizing key aspects of urban environments, from transportation options to patterns of land use, highlighting both community strengths and areas for improvement.
Recognizing that there is no universal approach to fostering urban mobility and reducing climate impact, HeiGIT tailors geospatial indicators to reflect the distinct walkability needs of each city. These tailored indicators enable urban planners, NGOs, and other stakeholders to address specific walkability challenges in their cities, factoring in unique cultural, environmental, and socio-economic conditions.
Using a co-creation process, we are building a walkability index that can adapt to diverse regions and contexts worldwide. Through a series of workshops, we identify which factors are most important to consider for walkability in that specific context. For example, in one city, sidewalk availability and safety might be top priorities, while in another, factors such as air quality or accessibility might take precedence. These factors can range from the presence of benches and rest areas, to the condition of surfaces and the level of traffic. Other indicators, like the proximity of essential services or the extent of lighting, may also play a critical role in enhancing walkability.
This adaptable approach is exemplified in cities like Lagos, Nigeria. Here, despite the lack of formal sidewalks, low car traffic allows pedestrians to walk comfortably on the main streets. This phenomenon is far from unique. In many parts of the world, areas with very low car traffic naturally become walkable, even without traditional sidewalks. Such contexts challenge conventional definitions of walkability and demonstrate the importance of considering local conditions when assessing what makes an environment “walkable.”
Methodology and Data Collection
We are working on a walkability index that will be globally accessible but locally adaptable, leveraging open data and additional tools. OpenStreetMap (OSM) is our primary data source, providing detailed information about urban transportation infrastructure and its properties. This crowdsourced, open-access data enables diverse cities to incorporate walkability into urban planning and design, ensuring that each city’s unique landscape and requirements are met.
We currently have developed three separate indicators that use OSM data to assess different aspects that influence how walkable streets and paths are.
- Our first indicator, “Walkable Path Categories”, answers the question: “Who do I have to share this path with?”. It is a classification of all potentially walkable ways (OSM tag “highway=*”) and polygons (e.g., train platforms, squares, parking lots, etc.) according to whether they are designated for exclusive use by pedestrians (e.g., most sidewalks), or they are shared with bikes (e.g., paths tagged as “foot=designated & bicycle=designated & segregated=no”), or motorized traffic of different speeds (using OSM tag “maxspeed”). A street can still be relatively walkable even when shared with cars, as long as these have to drive very slowly and pedestrians have priority (e.g., “highway=living_street”). But as cars gain priority over pedestrians and drive faster, walking quickly becomes unsafe and less enjoyable.
- A second indicator, “Surface Quality”, answers the question: “How comfortable is it to walk on this surface”, and describes how smooth the surface of walkable paths is. This is also an indicator of accessibility, especially important for people with limited mobility, or those who need to use wheelchair or wheeled walking frames. The indicator classifies paths into different surface quality levels using information of “smoothness” OSM tags. When these are not available, we also use the tags “surface” (e.g., asphalt, concrete, cobblestone, gravel) and “tracktype”.
- Our third indicator, “Connectivity”, answers the question: “How directly do I get where I want to go?”, and captures how directly streets are connected to other locations in the surrounding area. Specifically, our indicator measures the fraction of nearby locations that can be reached within a short walk (e.g., 15 minutes) from a given street segment, relative to the number of locations that could be accessed “as the crow flies” (i.e., in a straight line). The better connected paths are in a street network, more locations are easily accessible by foot, which makes walking a more attractive transportation mode.
Future Developments
We continue to refine our current indicators and plan to develop new ones. For example, an upcoming indicator of “Greenness” will combine OSM-data with Sentinel-2 satellite imagery to calculate the average Normalized Difference Vegetation Index (NDVI, a proxy for vegetation density) of a small area surrounding each street. Greener streets, with more trees and surrounding parks and gardens, are more pleasant and attractive to walk through. We are also planning indicator to calculate the levels of noise and air pollution of different streets, which also impact how pleasant it is to walk through a given area.
So far, our indicators are limited by the quality and completeness of OSM data. To enhance the reliability and scalability of our walkability index, we plan to integrate remote sensing data and street-level imagery from the Mapillary platform. Remote sensing and street-view data offer complementary insights, helping confirm and correct OSM tags. Combining additional data with OSM tags will allow us to refine our classifications, ensuring that path types and traffic conditions are as accurate as possible.
Looking Ahead
The walkability index is still under development, and future research will assess how well our adapted analysis of pedestrian friendliness aligns with the real-world experiences of different population groups. Ultimately, we plan to share this index via an online platform, providing cities with the tools they need to create more climate-conscious urban environments.
If you are interested in this work, want to collaborate, or help develop new tools, contact Kirsten at kirsten.vonelverfeldt@heigit.org.
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The QGIS ecosystem thrives thanks to its active community and an impressive array of plugins. These plugins continuously add new features to QGIS, making it easy and efficient for users to perform geospatial processing.
Here is a list of the most popular and useful QGIS plugins, along with insights on how they can elevate your GIS work.
1. QuickMapServices
For continuously updated catalogs of basemaps and a user-friendly interface
Whether you’re a seasoned GIS professional or just starting out, QuickMapServices (QMS) makes it simple to add the perfect basemap to your project with just a few clicks. It provides access to many different types of maps, including Google, Bing, and OpenStreetMap (OSM) layers. QMS, which has been downloaded over 7.2 million times, has an extensive catalog and customizable features, making it an essential plugin for any QGIS user.
QuickMapServices
2. Aino
For simplified, AI-enhanced OpenStreetMap (OSM) data capabilities
Aino is a powerful plugin designed for users who want to harness the full potential of OSM data. It makes easier to use OSM data. The plugin offers tools to efficiently download, parse, and integrate OpenStreetMap layers directly into QGIS. It has simple options for filtering and selecting specific OSM features (like roads, buildings, or points of interest). It makes it easy to work with open geospatial datasets.
One of the standout features of Aino is its AI-driven capability to convert natural language prompts into relevant OSM data. For example, you can simply type “parks in Barcelona,” and Aino will gather the corresponding vector layers for you. This no-code approach streamlines the data-gathering process, allowing users to focus more on analysis and less on technical details.
More use-cases:
https://youtube.com/shorts/zBmSAjCR-JU?si=ElVsIKLZThUTwbnE
https://youtube.com/shorts/V2ZrkZO3M0s?si=VKq4yTBTUBsYJEsX
3. MMQGIS
For user-friendly data manipulation and extensive processing tools beyond the standard toolbox
MMQGIS is a standout plugin for QGIS users who need powerful tools for data manipulation and processing. It offers a variety of tools for working with data, such as geocoding, attribute manipulation, and spatial operations. MMQGIS is perfect for bulk processing tasks, such as merging layers, geocoding addresses, and creating spatial joins. MMQGIS simplifies these processes with ease.
What truly sets MMQGIS apart is its user-friendly interface and the extensive range of tools it provides, which are often not available in the standard Processing toolbox. For instance, the geocoding feature allows you to convert a list of addresses into geographic points quickly or reverse geocode points back into addresses. This capability is invaluable for urban planners, researchers, and anyone working with large datasets who needs to streamline their workflows.
4. Data Plotly
For creating stunning, interactive visualizations directly from spatial data
Data Plotly is an exceptional plugin if you want to present complex datasets in a visually appealing and highly interactive way. This plugin integrates advanced plotting capabilities into QGIS, using the Plotly library. It is great for creating interactive, publication-quality charts and graphs directly from your spatial data.
With Data Plotly, you can easily create a variety of D3-like plots—such as scatter plots, bar charts, and histograms—right within QGIS. One of its standout features is the ability to link your plots with the QGIS map canvas. This means that when you select a point on your chart, the corresponding feature on the map is automatically highlighted, making it easier to explore relationships between your data and its geographic context.
More info: https://youtu.be/-fz9wHEBoR4?si=o01EZAgWMiZpfwup
5. TimeManager
For studying temporal patterns through engaging animations
TimeManager is a fantastic plugin that brings a dynamic element to your spatial data by allowing you to analyze and visualize changes over time. Its ability to create engaging animations that showcase how data evolves makes it the perfect tool for researchers, urban planners, and environmental scientists who need to study temporal patterns.
With TimeManager, you can easily set up a time slider that lets you filter your datasets based on specific time intervals. This means you can track the progression of events, such as the spread of urban development or changes in land use, in a visually compelling way. The plugin also allows you to synchronize multiple spatio-temporal layers, giving you full control over the animation speed and step size.
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6. Profile Tool
For creating detailed elevation profiles from raster layers to analyze terrain
Profile Tool creates elevation profiles from raster layers, making it an essential tool for anyone analyzing terrain. It allows users to see changes in height along a line, giving you a clear understanding of the landscape’s features.
This plugin is particularly useful for geologists, environmental scientists, and urban planners who need to stufy how terrain varies over distance. With Profile Tool, you can simply draw a line on your map where you want to analyze elevation, and it will generate a profile that shows the ups and downs of the terrain along that path. This feature is invaluable for assessing slopes, valleys, and ridges, helping you make informed decisions based on the topography.
7. OSMDownloader
For quickly downloading and importing OpenStreetMap data in a variety of formats
OSMDownloader is a fantastic plugin for QGIS that simplifies the process of downloading and importing OpenStreetMap (OSM) data. Its user-friendly interface allows you to quickly select an area of interest by drawing a rectangle on the map.
Once you’ve defined your area, OSMDownloader handles the rest. It not only downloads the OSM data but also automatically loads it into QGIS as layers. Another highlight of OSMDownloader is its ability to download data in a variety of formats, making it flexible for different project requirements.
8. QGIS2ThreeJS
For creating stunning 3D visualizations using WebGL technology
QGIS2ThreeJS is an impressive plugin that allows users to create stunning 3D visualizations of their QGIS layers using WebGL technology. Use it to transform complex geospatial data into interactive 3D maps that can be easily shared and viewed in any web browser.
For example, with QGIS2ThreeJS, you can visualize Digital Elevation Models (DEMs) and vector data, allowing you to see terrain features and spatial relationships in a whole new dimension. The ability to export these visualizations as HTML files means you can showcase your work in presentations or share it with stakeholders without needing specialized software.
9. SRTM Downloader
For easily downloading SRTM (Shuttle Radar Topography Mission) elevation data
STRM Downloader does exactly what it sounds like it does – it makes it easier to download SRTM elevation data. People who work with terrain and topography will find this plugin very useful because it allows them to obtain elevation data quickly, without needing external tools or complicated procedures.
With SRTM Downloader, you can easily define your area of interest by drawing a rectangle on the map or entering coordinates, and the plugin will automatically download the relevant SRTM data tiles in .hgt format. This means you can get accurate elevation data for your specific project area in just a few clicks.
10. LAS Tools
For batch-processing of LiDAR data directly within QGIS’s processing framework
The LAS Tools plugin allows users to process LiDAR data directly in QGIS. Its robust set of functionalities are specifically designed for working with point cloud data, including classification, thinning, and exporting options.
One of the standout features of LAS Tools is its ability to handle large datasets efficiently. With over 50 powerful tools at your disposal, you can easily perform complex operations like extracting ground points, generating digital elevation models (DEMs), and filtering noise from your LiDAR data. LAS Tool’s batch-processing capabilities allow you to automate repetitive tasks, saving you significant time and effort.
11. Value Tool
For displaying the values of multiple raster layers simultaneously
Value Tool is a handy plugin for QGIS that allows users to display the values of multiple raster layers simultaneously. Its ability to provide real-time data insights right at your mouse’s position on the map makes it incredibly useful for comparative analysis of raster datasets, such as temperature or precipitation layers.
Another standout aspect of Value Tool is its flexibility; it supports not just raster layers but also mesh layers, allowing for a broader range of applications in your analyses.
This plugin is particularly beneficial for researchers, environmental scientists, and anyone involved in spatial analysis who needs to understand how different raster layers interact with one another.
These QGIS plugins show how useful and powerful the QGIS platform is. If you’re a developer, data analyst, or GIS professional, you can improve your work and get better results by using these.
Check out these plugins today to transform your geospatial workflows and push the boundaries of what’s possible with QGIS.
Are you familiar with or have you used any of these tools? Which plugin do you find most useful?
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