There are multiple definitions in the scientific literature for the term ‘bikeability’. Gehring describes the suitability of cycling by considering several properties of the built environment that can promote or hinder cycling (Gehring 2016). To calculate the bikeability index, there are various approaches in the analysis process, including how to obtain the necessary spatial data, as it requires a significant amount of spatial data. Spatial data for calculating bikeability is often quite difficult to acquire. However, one open data platform, OpenStreetMap, offers the best alternative as a spatial data source that sufficiently supports bikeability index calculations. This was done by Heinemann in her thesis. Heinemann used a different approach in her bachelor thesis (Heinemann 2022). Her analysis of bicycle friendliness in Heidelberg relies entirely on open-source OpenStreetMap (OSM) data.
The calculation method used in this article is essentially similar to the method used by Heinemann but with slight modifications. The workflow used is as follows:
- All databases are sourced from OpenStreetMap
- QGIS is used to calculate the index for each indicator
- The study location does not take traffic hours into consideration
- The analysis is conducted only in urban areas with adequate traffic facilities
- Calculations are carried out using 100×100 m hexagonal grid units.
From the workflow above, it can be seen that the entire process uses open mapping tools, both for analysis and data sources. Urban areas were chosen because of the level of detail of the data in OpenStreetMap and its superior data accuracy.
Fig 1. Existing OSM Data in Central Jakarta
Based on the indicators used by Heinemann (2022), there are five indicators for calculating the bike-ability index, namely:
- Length of biking infrastructure
- Proportion of natural areas
- Density of bike parking spots
- Density of potential destinations
- Separation from cars
This article uses all the elements used by Heinemann but with a simpler method. The indicators used in this article are grouped into two categories:
- Bike Facilities: Includes all types of facilities that support cycling activities, such as bike lanes, bike parking, and bike repair shops, as well as cycling safety in terms of vehicle speed around the bike lanes, road width around the bike lanes, and the number of lanes.
- Origin and Destination: Includes all types of activities that could generate or attract cycling trips, such as office centers, malls, tourist sites, transit stations, green spaces, CBD areas, and apartments.
All these variables are represented in hexagonal cells that are scored. The accumulation of all variables then becomes the final score for the two categories of indicators used in this article.
Fig 2. Bikeability Index Scoring Illustration
With the workflow mentioned above, all the required data is downloaded through the OpenStreetMap Overpass server. Knowledge related to OSM tagging is essential here to obtain the desired data that corresponds to the Bikeability Index indicators. Once all the data is acquired, the next step is to score the variables using the QGIS application. Scoring begins with the creation of 100x100m hexagonal cells for the study area. Then, each cell is checked for the completeness of each variable from the two indicators mentioned above. Each cell will have different values. For example, if one cell contains an apartment, a bike lane, a bike repair shop, a road with relatively good cycling safety, and several transit points for public facilities, it is highly likely that the score for that cell will be high when accumulated. The score for each variable ranges from 1 to 5.
Fig 3. QGIS Bikeability Index Scoring Table
The final calculation essentially shows that Central Jakarta has several optimal spots for cyclists and other non-motorized vehicles that can use the bike lanes. This is due to the high number of activity centers in Central Jakarta, such as office buildings, shopping centers, malls, apartments, public facilities, and tourist destinations. Additionally, several corridors are supported by adequate bike lanes, both with and without separators.
From the final bikeability index map, it can be seen that there are quite a few dark-colored hexagonal cells, covering almost every corner of Central Jakarta. This analysis suggests a few things: first, the importance of maintaining bike lanes in areas with a high bikeability index. Maintenance is essential to preserve the facilities and add safety features such as separators or better-marked bike lanes, including painting and lighting to make them more visible at night.
Second, the analysis highlights potential locations for developing bike lanes. If there are dark-colored hexagonal cells that currently lack bike facilities, this can serve as a recommendation for the local government to develop bike lanes in those corridors. An area can have a high bikeability index due to the high number of activity centers and the availability of sufficiently wide roads around the area to accommodate bike lanes.
Fig 4. Final Bikeability Map of Central Jakarta
From the analysis example above, we can see that OpenStreetMap clearly provides a good overview for urban planners regarding the direction of policy expansion, such as policies related to bike lanes. The availability of data on OpenStreetMap offers a quick, easy, and free alternative for anyone to observe phenomena resulting from spatial analysis. From the case study above, we can see how the expansion of bike lane provision in Central Jakarta has been well-executed and understand the current conditions.
However, this article is merely a simple example of how OSM data can be used for spatial analysis of the bikeability index. There are still many shortcomings in terms of the depth of analysis and methods used. The use of literature is also limited to only a few studies that apply relatively simple methods. Collaboration among researchers in utilizing OSM data is highly needed to understand how far OSM can address the challenges researchers face concerning data availability.
The OpenStreetMap Indonesia community is here to support you in mapping and analyzing data that contributes to critical areas such as food security, land use, and transportation routes, our team can assist you every step of the way. By leveraging OpenStreetMap’s extensive tools and data, you can help create a more informed and sustainable environment for everyone. Together, we can enhance our understanding of local needs and ensure that essential services and resources are accessible to all. Reach out to us today, and let’s work together to make a meaningful impact!