Are the Poor Digitally Left Behind? Indications of Urban Divides Based on Remote Sensing and Twitter Data
Abstract
:1. Introduction
2. Background and Rationale
3. Experimental Set-Up: Materials and Methods
3.1. Selection of Experimental Cities
- We select cities which feature a significant share of urban poor documented in literature or official census data.
- We select cities containing building morphologies characteristic for slums, which are in line with the conceptual ontology presented by [20] and the related empirical basis demonstrated by [15]. This physical appearance differs to formal settlements and can be classified by VHR optical remote sensing data.
- We select cities at different cultural areas and continents across the globe.
3.2. Mapping the Economic Divide
3.2.1. Remote Sensing Data
- Optical sensors, such as QuickBird or WorldView, provide geometric resolutions of 1m and better and thus, the urban morphology is represented by individual buildings. We apply these data for the delimitation of morphologic slums. Figure 2a illustrates the complex urban environment by contrasting geometric, planned, formal building structures with non-regular, unplanned, non-formal building structures of morphological slums.
- We use radar data from the TerraSAR-X and TanDEM-X missions at Stripmap mode providing geometric resolutions of 3 m. For urban landscapes spatial complexity of varying objects within small areas is characteristic. In radar data this is represented in highly textured image regions of strong directional, non-Gaussian backscatter due to double bounce effects. This information is used along with the intensity information to delineate ‘settlements’ from ‘non-settlements’ using an unsupervised image analysis technique, for technical details we refer to [43]. The accuracies of the settlement classification in dense urban areas (as in our case studies) have been measured beyond 90% [44].
3.2.2. Mapping Morphologic Slums versus Formal Settlements
3.3. Mapping the Digital Divide
3.3.1. Twitter Data
3.3.2. Filtering and Processing of Twitter Data
3.3.3. Statistical Spatial Variations of Tweet Densities between Morphological Slums and Formal Settlements
3.3.4. Detection of Digital Hot and Cold Spots
3.4. Temporal Analysis
4. Results
4.1. Data and Mapping Results
- In general, we classify 274,184 hectare of cumulated settlement areas for all our sample cities. We find that only 5.54% of the settlement areas are occupied by ‘morphologic slums’. However, for the area share of morphologic slums of the total settlement area we observe a strong variation across cities from 18.90% in Caracas to only 0.11% in Lisbon (Table 1).
- The preprocessed twitter data set contains 3.73 million geolocated tweets cumulated for all sample cities. We find that twitter activity varies significantly across cities, with Manila featuring more than 2 million tweets vs. Dhaka with only about 30,000 tweets within the time period of monitoring. Beyond, we also find that a relatively small share of tweets of 2.7% is localized in morphologic slum areas. However, we also observe strong variations of shares of tweets in morphologic slums across cities from 8.74% in Caracas to only 0.07% in Lisbon (Table 1).
4.2. Variance Analysis of Tweet Densities Across Cities and Within Cities (between Morphological Slums and Formal Settlements)
4.3. Spatial Statistics of Digital Hot Spots and Cold Spots
4.4. Temporal Signatures of Twitter Activities
5. Discussion
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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City | Settlement Area Based on Remote Sensing Data | Number of Recorded Tweets and Shares of Tweets | Number of Twitter Users | |||
---|---|---|---|---|---|---|
Total (ha) | Morphologic Slum (%) | Total (n) | Morphologic Slum (%) | Total (n) | Morphologic Slum (%) | |
Dhaka | 20,817 | 6.79% | 28,395 | 3.11% | 17,773 | 3.1% |
Mumbai | 23,940 | 14.21% | 133,161 | 3.5% | 79,540 | 2.99% |
Manila | 47,552 | 6.09% | 2,038,719 | 1.61% | 1,265,235 | 1.36% |
Caracas | 16,333 | 18.9% | 233,876 | 8.74% | 108,346 | 7.81% |
Rio | 36,191 | 6.66% | 999,525 | 4.55% | 583,973 | 4.15% |
Cairo | 52,482 | 2.53% | 122,192 | 0.43% | 71,325 | 0.3% |
Capetown | 40,673 | 1.49% | 87,779 | 0.64% | 50,962 | 0.47% |
Lisbon | 36,196 | 0.11% | 150,861 | 0.07% | 98,136 | 0.04% |
Weekday | Weekend | ||||||||
---|---|---|---|---|---|---|---|---|---|
SOD | EOD | LOD | RMSE | SOD | EOD | LOD | RMSE | ||
Mumbai | Formal Settlement. | 7.08 | 23.06 | 15.98 | 0.039 | 8.09 | 23.06 | 14.97 | 0.042 |
Morphological slum | 7.08 | 23.06 | 15.98 | 0.073 | 7.08 | 23.06 | 15.98 | 0.080 | |
Manila | Formal Settlement. | 6.07 | 22.04 | 15.98 | 0.032 | 7.08 | 22.04 | 14.97 | 0.033 |
Morphological slum | 16.04 | 23.06 | 7.01 | 0.044 | 7.08 | 23.06 | 15.98 | 0.056 | |
Caracas | Formal Settlement. | 5.06 | 22.04 | 16.99 | 0.052 | 6.07 | 22.04 | 15.98 | 0.048 |
Morphological slum | 4.04 | 22.04 | 18.00 | 0.058 | 5.06 | 22.04 | 16.99 | 0.057 | |
Rio | Formal Settlement. | 6.07 | 23.06 | 16.99 | 0.035 | 8.09 | 23.06 | 14.97 | 0.028 |
Morphological slum | 7.08 | 23.06 | 15.98 | 0.038 | 8.09 | 1.08 | 16.99 | 0.043 |
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Taubenböck, H.; Staab, J.; Zhu, X.X.; Geiß, C.; Dech, S.; Wurm, M. Are the Poor Digitally Left Behind? Indications of Urban Divides Based on Remote Sensing and Twitter Data. ISPRS Int. J. Geo-Inf. 2018, 7, 304. https://doi.org/10.3390/ijgi7080304
Taubenböck H, Staab J, Zhu XX, Geiß C, Dech S, Wurm M. Are the Poor Digitally Left Behind? Indications of Urban Divides Based on Remote Sensing and Twitter Data. ISPRS International Journal of Geo-Information. 2018; 7(8):304. https://doi.org/10.3390/ijgi7080304
Chicago/Turabian StyleTaubenböck, Hannes, Jeroen Staab, Xiao Xiang Zhu, Christian Geiß, Stefan Dech, and Michael Wurm. 2018. "Are the Poor Digitally Left Behind? Indications of Urban Divides Based on Remote Sensing and Twitter Data" ISPRS International Journal of Geo-Information 7, no. 8: 304. https://doi.org/10.3390/ijgi7080304