{"id":39796,"date":"2026-04-19T21:47:31","date_gmt":"2026-04-20T01:47:31","guid":{"rendered":"https:\/\/studentwork.prattsi.org\/infovis\/?p=39796"},"modified":"2026-04-19T21:50:03","modified_gmt":"2026-04-20T01:50:03","slug":"a-spatial-exploration-of-bangalores-traffic-crisis","status":"publish","type":"post","link":"https:\/\/studentwork.prattsi.org\/infovis\/labs\/a-spatial-exploration-of-bangalores-traffic-crisis\/","title":{"rendered":"A Spatial Exploration of Bangalore\u2019s Traffic Crisis"},"content":{"rendered":"\n

For the first time (and hopefully not only) reader of this piece, you can refer to my previous piece<\/a>, but I\u2019m happy to catch you up on the context on my series of visualization experiments. Bangalore, as a city has an innovative and booming start-up and tech culture, but it remains difficult to adjust to one of the most challenging issues: traffic. Globally, Bangalore ranks as the 2nd<\/sup> most congested city (source<\/a>). Working with a dataset from Kaggle<\/a>, I\u2019m exploring ways to visualize the traffic problem and address the multi-faceted nature of this layered problem. While charts and graphs serve as helpful ways to visualize trends with different issues, a map, given the larger theme of a city, serves as a better way to visualize everything related to traffic.<\/p>\n\n\n\n

The dataset provides entries from 2022 to 2024, recording measures such as the congestion level, road capacity utilization, environmental impact etc. on a specific date at a specific road\/intersection. Therefore, it provides different factors to consider from an urban planning standpoint, such as environmental impact, road safety and overall traffic trends. This project was guided by wanting to spatially visualize all these metrics.  <\/p>\n\n\n\n

Formatting the Data<\/h2>\n\n\n\n

As a first step, the dataset needed additional metrics in order to be spatially visualized. The first step involved using a kml file created on Google Maps, wherein 4 key roads were marked out as points (Hebbal, Marathahalli, Sarjapur, and Silk Board) to visualize the outer ring road in Bangalore (where the tech corridor lies and constitutes one of the most congested roads in the city). Another option was to work with a shape file of Bangalore; however, this marks out the wards and their boundaries as laid out by the municipal corporation (BBMP).<\/p>\n\n\n\n

Here came the first issue- polygon shapes of wards do not align with roads\/intersections of the main dataset. An alternate approach would be to look at the broader areas in the dataset (neighborhoods such as Koramangala, Indiranagar etc.) and perhaps see if these could be the broader BBMP wards, and visualize traffic within these wards. But this wouldn\u2019t be the best of the data because the larger wards themselves are more than the few roads considered in the dataset, and roads and intersections can span across wards. Essentially, the spatial structure of the ward dataset doesn\u2019t align with the csv dataset of roads and intersections.<\/p>\n\n\n\n

A truer use of the data would involve mapping out the intersections as points. On that note, generative ai (namely Claude) was used to geocode the csv dataset. As opposed to manually locating and entering the latitudes and longitudes, AI was essential in automating the geocoding process, allowing for more focus on the visualization portion.<\/p>\n\n\n\n

\"\"
The original dataset only had columns A to P. Claude was used for the geocoding to add columns Q and R.<\/em><\/figcaption><\/figure>\n\n\n\n

Visualizing the Data<\/h2>\n\n\n\n

With the geocoding automated, I proceeded to build out maps. I began by wanting to visualize congestion levels, to get a sense of how and where the bottlenecks are present in the city. This can be found here<\/a>.<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

In this map, points indicate the different roads and intersections in the city, with a darker color indicating higher congestion levels. The map depicts relatively lower congestion levels in the northwest of the city and higher congestion in central bustling areas such as Koramangala and Indiranagar (namely the Sony World Junction Road and CMH Road). The base of the map is lighter and with only boundaries and neighborhoods marked out. Users can hover over each point with a tooltip detailing the necessary information such as the road’s name and the corresponding congestion level. <\/p>\n\n\n\n

This is similar to the bar chart I created in my previous piece<\/a>, except the bar chart showcases the traffic across neighborhoods, not roads. In any case, a map serves to illustrates more, allowing for more contextualization in a spatial manner.<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

The struggle with the map was assessing what information was needed and what wasn\u2019t. I experimented with a dark background as well as normal background, eliminating the text, so that the core function is restricted to hovering, and the sole visual attention is on the congestion points of the concerned roads.<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n
\"\"<\/figure>\n\n\n\n

\u00a0A darker background perhaps allows the red to be perceived better, and can be easily understood by someone familiar with Bangalore. I would like to continue my user testing to assess whether a more minimal map is preferred, or is users would like to see the text, with general roads, neighborhoods and points of interest marked out.<\/p>\n\n\n\n

On similar lines as congestion levels, I began to create maps for incident reports, to understand public safety in the city and gauge areas of more concern when it comes to driving. A lighter red indicates the roads with fewer incident reports, while a darker red showcases higher incident reports. Here I used the sum of incident reports from 2022 to 2024, to put into perspective the need for safety.<\/p>\n\n\n\n

A Map for Everything<\/h2>\n\n\n\n

As opposed to creating different maps for all the factor related to traffic, I decided instead to create a single map, wherein users can visualize every factor of the dataset within one space. Here I worked with adding calculated parameters and calculated fields on Tableau to integrate everything into a single map.<\/p>\n\n\n\n

I duplicated the final version with the dark background and light background because as discussed previously, further clarity via user research on which base map works better will help guide this (workbook with dynamic maps<\/a>).<\/p>\n\n\n\n

In essence, this map depicts all the roads and intersections in the dataset, and users can access the dropdown to visualize every metric. They can look at congestion levels, environmental impact, incident reports, travel time index, road capacity utilization etc. This dynamic map allows for a comprehensive visualization of the dataset, giving users the chance to look at everything on one interface. After selecting the metric to visualize, users can hover on each point and read the road\u2019s name, the area\/neighborhood and the value of the metric. The scale remains the same with a darker red indicating a higher level of the metric, and a lighter shade depicting a lower level. <\/p>\n\n\n\n

Perhaps changing the color palette depending on the metric, or even the use of icons could help. More customization and improving that visual aspect of the map could be more personal and perhaps also allow for an emotional relationship between the user and map. I would be especially keen on seeing whether that would be well received by native Bangaloreans. <\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

Working with the parameters and calculated fields also allowed me to introduce finer details such as showing the average speed in kilometers per hour, and the congestion levels as percentages etc. However, for every metric, the values taken are the average. As a next step, I would be interested in seeing if I could use the sum for things like incident reports from 2022 to 2024. I wonder if this would be a poor choice, wherein the map requires users to look at sums and averages within one space, or whether it should be consistent as a whole. It\u2019s a question I\u2019d love to explore.<\/p>\n\n\n\n

The map really allowed for a greater use of the dataset, which I wasn\u2019t able to do with charts and graphs. Representation of traffic in a city seems best visualized through a map, and it allows for a more immersive experience in some ways. It especially helps on the note that a map can be created for both, people familiar and unfamiliar with Bangalore. That being said, I refer back to the limitation of not having narrowed down on what background map works best, and hope to answer this soon.<\/p>\n","protected":false},"excerpt":{"rendered":"

For the first time (and hopefully not only) reader of this piece, you can refer to my previous piece, but I\u2019m happy to catch you up on the context on my series of visualization experiments. Bangalore, as a city has an innovative and booming start-up and tech culture, but it remains difficult to adjust to…<\/p>\n","protected":false},"author":4595,"featured_media":39807,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":true,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[149,341],"tags":[1933,1934,115,5,43,31,1935,123,502,377],"coauthors":[1919],"class_list":["post-39796","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-labs","category-maps","tag-bangalore","tag-city","tag-data-visualization","tag-information-visualization","tag-map","tag-maps","tag-spatial","tag-tableau-public","tag-traffic","tag-urban-planning"],"jetpack_featured_media_url":"https:\/\/i0.wp.com\/studentwork.prattsi.org\/infovis\/wp-content\/uploads\/sites\/3\/2026\/04\/City-Wide-Average-Congestion-Levels-Dark-Background-1-1.png?fit=2400%2C2032&ssl=1","jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/paBdcV-alS","_links":{"self":[{"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/posts\/39796","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/users\/4595"}],"replies":[{"embeddable":true,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/comments?post=39796"}],"version-history":[{"count":1,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/posts\/39796\/revisions"}],"predecessor-version":[{"id":39806,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/posts\/39796\/revisions\/39806"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/media\/39807"}],"wp:attachment":[{"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/media?parent=39796"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/categories?post=39796"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/tags?post=39796"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/coauthors?post=39796"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}