A Byte of my 2.2-lb Brain

Just sharing stuff…

Urbanism in the Philippines

A Different Look at the Philippine Map

In the Philippine map below, we show a month’s worth of geotagged tweets collected via Twitter’s Public Streaming API spritzer, which allows authenticated users to access 1% of all public tweets worldwide. Two things immediately came to mind after I generated the image: (1) the correlation of the tweet distribution to the Gross Regional Domestic Product (GRDP), which is just the GDP measured at regional levels, and (2) the correlation of the distribution to Population Density.


“Philippine Geotagged Tweets Map” by EF Legara / CC BY 4.0

Is it possible to just look at the number of tweets per region and infer the population density at that region? Does it make sense to obtain a rough estimate of  the GRDP of a locality based on the number of geotagged tweets that fall onto the area?

To explore these ideas, I show two heat maps below: population density and GRDP. At first glance, that may actually seem to be the case—that Twitter data can be used as proxy for GRDP and population density (at least for the Philippines). However, the hypotheses need further investigation/validation since the heat maps show the trends for the whole of each region, but the PH Twitter map presents a much higher resolution visualization. Nevertheless, the results look encouraging. Hence, the thing I did next was to normalize the tweet counts per region—by doing so, we can finally compare apples with apples.

What else can the Twitter data represent? I have often told friends and colleagues that Twitter may not be a good survey source especially for a developing country like the Philippines where there is an existing issue on the penetration rate of social media platforms. But, if more citizens use Twitter or Facebook, does it mean that the city is technologically more “advanced” (in a relative sense)? Can it represent technological advancement? How about urbanization?

Do more urbanized cities have more dynamic Twitter activities?

Then I remembered NASA’s city light data from its Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS). The city light data have been used to map urbanization—where “brighter” regions (not necessarily more populated ones) have been identified to be more urbanized. The thought got me really excited!

Below, the Philippine Twitter map I generated (left) is placed side-by-side with the map of the Earth’s city light capture (right, zoomed in on the Philippines) [1].

Indeed, it looks like the Twitter data can represent urbanization, and it’s beautiful!


“Philippine Geotagged Tweets Map and Earth’s City Lights (Philippines)” by EF Legara / CC BY 4.0. NASA’s city light map (right) is cropped from original.


I then focused on the three Philippine metropolises that I thought are more urbanized than the rest of the Philippines: Metro Manila, Metro Cebu, and Metro Davao. The first two rows show the road networks of the metros—the only difference is in the rendering; on the other hand, the last row shows the mapped tweets. We can see from the figures that for Cebu and Davao, the tweets are concentrated within the mall areas—two things: (1) free wifi  and (2) activity/event that’s worth tweeting about.


“Urbanism in the Philippines” by EF Legara / CC BY 4.0 (click on the image to enlarge)

I find it interesting that there also seems to be a correlation between the intricacy of the road networks and the number of tweets. This is definitely worth investigating!


“Urbanism in the Philippines” by EF Legara / CC BY 4.0

Transport Planning

In the image below, I added the rail/rapid transit systems stations (purple) including some stations of the PNR.

Some idea on transport planning: Maybe the generated Twitter map can also be used as a guide, at least for Metro Manila, in extending/expanding our rapid/rail transit systems, e.g. Where should we place the interchanges?

“Metro Manila Tweets and its RTS” by EF Legara / CC BY 4.0

“Metro Manila Tweets and its Rail Transit System” by EF Legara / CC BY 4.0

Other details

  • I wrote the scripts in both Python (Twitter data collection) and [R] (all the mapping).
  • Thanks to Mapzen and OpenStreetMap for the shapefiles used in the figures.
  • The images are licensed under CC 4.0. You are free to Share — copy and redistribute the material in any medium or format under the following terms:


[1] Data courtesy Marc Imhoff of NASA GSFC and Christopher Elvidge of NOAA NGDC. Image by Craig Mayhew and Robert Simmon, NASA GSFC. Date Data: October 1, 1994 – March 31, 1995


Legara E. Urbanism in the Philippines. A Byte of my 22-lb Brain. 2015. Available at: http://erikafille.ph/2015/09/10/urbanism-in-the-philippines/. Accessed Month DD, YYYY.

One comment on “Urbanism in the Philippines

  1. Wilhansen Li
    December 10, 2015

    Nice visualization and analysis!

    The similarity of road networks and tweets could be generated by people stuck on the road and tweet to pass time. So filtering the tweets to topics that are indoors like food pics might undo the similarity (also this might explain the tweets in BGC, SM MOA, and a portion of Tomas Morato). Or checking the tweets that coincide with major roads (EDSA, Q. Ave) might come up with interesting trends (it’s possible that most of them will be traffic-related).


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This entry was posted on September 10, 2015 by in Blog, Philippines and tagged , , , , , .
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