| Literature DB >> 36250197 |
Alberto Federico Ogas-Mendez1, Xuanda Pei2, Yuzuru Isoda2.
Abstract
The COVID-19 outbreak magnified territorial inequalities and increased vulnerability among low-income groups. Inhabitants in informal settlements are structurally disadvantaged in coping with communicative diseases such as the COVID-19 pandemic. Despite that, the pandemic has been accompanied by the proliferation of informal settlements. This study explores how the pandemic caused the squatting on new land with the case of "Los Hornos" in suburban Buenos Aires. We used a random forest algorithm and Google Earth Engine to estimate the rapid growth of a new informal settlement from a series of satellite images from early 2020. We also conducted semi-structured interviews with inhabitants to investigate the link between squatting and COVID-19. The study revealed that squatting on new land during the pandemic was mainly due to economic difficulties, overcrowding in existing informal settlements in the metropolitan center, and speculation in the informal housing market. This case is an example of how the most vulnerable groups bore the brunt of the pandemic, how the households in the existing informal settlement were behaving similar to those in the formal housing market (i.e., away from the urban centers), and how the outbreak had also been an opportunity for collective action of squatting a new land to materialize.Entities:
Keywords: COVID-19; Google earth engine; Informal housing market; Informal settlements; Random forest
Year: 2022 PMID: 36250197 PMCID: PMC9554334 DOI: 10.1016/j.habitatint.2022.102688
Source DB: PubMed Journal: Habitat Int ISSN: 0197-3975
Fig. 1Informal Settlements in La Plata. The locations of informal settlements based on TECHO (2018), background image from Google Earth Engine.
Number of persons infected by COVID-19 in the CBA and its informal settlements in December 2020.
| City of Buenos Aires | Informal Settlements | |||
|---|---|---|---|---|
| Population | 3,075,646 est. | 250,000 est. | ||
| Infected | 171,097 | 5.56% | 17.694 | 7.07% |
Fig. 2Perimeter selection for the informal settlement “Los Hornos” in August 2019 and February 2022. Source: Google Earth Engine.
Sentinel 2 bands used, source: EESA.
| Spectral band | Central wavelength (nm) | Spatial resolution (m) |
|---|---|---|
| Band 2 blue | 490 | 10 |
| Band 3 green | 560 | 10 |
| Band 4 red | 665 | 10 |
| Band 8 NIR | 842 | 10 |
| Band 8a narrow NIR | 865 | 20 |
| Band 11 SWIR | 1610 | 20 |
Characteristics of the sample.
| Datasheet | |
|---|---|
| Area of Study | Los Hornos |
| Number of samples | 43 |
| Observation unit | Households |
| Collection date | November 2021 |
| Selection criteria | Two per segment (randomly selected) |
| Number of segments | 21 |
Themes and questions of the qualitative survey.
| Topic | Question |
|---|---|
Squatting | What is your primary motivation for squatting? What are the causes to decide to squat? Where was your previous residence? (location, type, and tenure) Do the ASPO and the less vigilance from the State encourage you towards squatting? |
Employment and housing | How did the mobility restrictions affect you? Are you working? If yes, are you working in the formal work market? Did you previously live in a squatter settlement? If yes, did you used to be a tenant or DF owner? |
COVID-19 and squatting | Does the rise in COVID-19 cases in your neighborhood encourage squatting the land? Do you believe moving to this informal settlement will reduce the risk of COVID-19? After the pandemic ends, are you planning to return to where you used to live? |
Informal hose market | Did you squat or buy the land? Did you get the land to live, rent, or sell? Was the decision to squat encouraged by an organization or third party? (Puntero) |
Fig. 3Squatter housing and densities, October 2020, June 2021, and January 2022. Squatter houses did not exist in April 2019. Source: Google Earth Engine.
Estimated changes in the housing area, April 2019 to January 2022. Confidence interval (CI) is based on user accuracy.
| Date | Area (m2) | CI (95%) | Difference | Growth Rate | |
|---|---|---|---|---|---|
| Lower | Upper | ||||
| April 2019 | 0 (0%) | – | – | – | – |
| October 2020 | 88,213(5.73%) | 81,649 | 94,705 | 88,213 | – |
| June 2021 | 239,115 (15.52%) | 221,323 | 256,795 | 150,902 | 171% |
| January 2022 | 299,701 (19.35%) | 277,401 | 321,861 | 60,586 | 25% |
Confusion matrix, UA, PA, OA, of the RF classification of the study area (2022).
| Classified data | House | Grassland | Bare ground | Total | PA (%) | UA (%) | PA(CI95%) | UA(CI95%) |
|---|---|---|---|---|---|---|---|---|
| House | 73 | 11 | 1 | 85 | 86.9 | 85.88 | 80.62–93.17 | 79.49–92.23 |
| Grassland | 8 | 89 | 7 | 104 | 87.25 | 85.58 | 81.59–92.91 | 79.8–91.36 |
| Bare ground | 3 | 2 | 40 | 45 | 83.33 | 88.89 | 74.54–92.12 | 80.73–97.05 |
| Total | 84 | 102 | 48 | 234 |
Overall accuracy 78.03%, Overall CI (95%) 74.63–81.43%.
Respondent profile: Employment and Housing.
| Current Employment | The previous type of housing | Total | |||
|---|---|---|---|---|---|
| Informal housing | Formal housing | ||||
| Total | Tenant | Total | Tenant | ||
| Informal employment | 25 | 21 | 0 | 0 | 25 (58%) |
| Formal employment | 7 | 3 | 3 | 2 | 10 (23%) |
| Unemployment | 5 | 4 | 0 | 0 | 5 (12%) |
| Retired | 2 | 0 | 1 | 1 | 3 (7%) |
| Total | 39 (91%) | 28 (65%) | 4 (9%) | 3 (7%) | 43 (100%) |
Respondents reasons for squatting, by previous housing and current employment.
| Previous housing | Current employment | Total | |||||
|---|---|---|---|---|---|---|---|
| Informal Total tenants | Formal | Informal | Formal/Retired | Unemp. | |||
| Economic | 22 | 20 | 4 | 22 | 3 | 1 | 26 (60%) |
| Overcrowding | 12 | 6 | 0 | 2 | 10 | 0 | 12 (28%) |
| Sell the land | 5 | 2 | 0 | 1 | 0 | 4 | 5 (12%) |
| Total | 39 (91%) | 28 (65%) | 4 (9%) | 25 (58%) | 13 (30%) | 5 (12%) | 43 (100%) |