Informal Networks
Simulating Informality in Amazonas, Brazil
Cellular Automata + Neural Networks Processing + T-SQL + BBMeta QGIS + Rhino
277570 number of informal jobs globally (000’s) The UNHabitat 2010/2011 report states that over 50% of the world’s population lives in urban areas, and almost the entirety of population growth in the world over the course of the next 30 years will stem from urban population growth (UNHabitat p.IX). Urban growth estimates predict that by 2050, 80% of the world’s population will live in megacities. As the Gini coefficient continues to increase in developed regions (US, China, Brazil, India, UK) and cities sprawl, the presence of informal networks and settlements will increase .
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277570
5131
Venezuela
5138
Argentina
7168
Peru
8247
Egypt
9307
Columbia
9642
Thailand
17172
Viet Nam
20258
Mexico
32493
Brazil
163014
India
Currently, partially due to a lack of publicly available block level data and difficulties experienced in attempting to map the informal, it is unclear how urban growth models take into account the growth of the informal sector. This proposal attempts to discover the key variables that determine informal settlement behaviour in order to model informal settlement growth and change.
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grocery store (illegal market) luxury high-rise (established shantytown) mayor (godfather) hard limit transfer function Frequently, when the discussion of informal settlements or informal economies takes hold of a conversation, the parlance dictates that the lexicon consists primarily of an aesthetic vocabulary. Vision prevails over the other senses and dominates the discourse, calling forth images of shantytowns, favelas, street-vendors, illegals, underground, and the outskirts. Our inability to see the depth and complexity of informal networks misleads us to think that informality and formality are binary rather than continuous, chaotic rather than structured.
fire escape (building code violation)
construction industry (squatter settlement)
raw materials (garbage)
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linear transfer function What if we replaced the aesthetic vocabulary with an analytical model to attempt to extract order and rules out of the complexity of informal systems? For instance, what if instead of considering discrete memes, the shantytown, favela, and underground to be indicators of neighborhoods, what if we employed building materials, occupancy types, and bed densities to indicate where on the formal-informal continuum an area of space falls? Could we begin to understand underlying explanatory patterns of “informality�?
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+ river + river bank + dirt road + sidewalk + building + undeveloped space
+ bed density +/- radio +/- refrigerator +/- tv +/- plumbing
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10 T
Informal Economies
Global Informal GDP Informal Brazil Amazonas
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14.3% informal percent of global economy Globally, 277.6 people were estimated to be employed in the informal sector in 2010. Brazil employed over 10.5 million workers in the informal economy in 2003. This represented a 10% growth rate from 1997. The data projected that over the course of the next ten years, the number of workers employed in the informal sector would grow by another 10%. Informal jobs are primarily in construction and trade and repair industry; however, informal jobs are beginning to appear in the tech and telcom industries as well.
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brazil
rest of the world combined european union united states informal china japan
$1.48 T 40%
$24.8 T
$16.4 T
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$15.7 T
$10.3 T
$8.3 T
$6.0 T
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40% informal percent of brazilian economy Informal companies within Brazil consisted of 88% individual self-employed workers. The remaining 12% were small business who hired employees and ran more like a “formal� business. 65% of production was conducted solely in the home of the business owner, 27% was conducted exclusively outside of the home of the business owner, and 8% of production was conducted both inside and outside the home. This implies that informal settlements are inherently highly mixed use clusters of productive activity.
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other activities ill defined activities education, health, and social services real estate and business services accommodation and food services other collective, social and personal transportation, storage, and communication material extraction and processing building construction trade and repair
? 3.5 m
1.8 m
1.7 m
.84 m
.83 m
.73 m
.68 m
.35 m
.09 m
?? .04 m
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167k number of informal jobs in Amazonas Amazonas’ informal economy focuses more on the food services industry than other regions. On average, workers in the informal sector in Brazil and Amazonas make approximately R$753 while employers make R$1,606. According to Forbes, this means that the average informal business owner actually makes more than the average worker in Brazil (R$1,279). Even though overall poverty levels in Brazil are greater than in the US, the implication is that the Brazilian poor are gaining wealth more quickly than the American poor.
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other activities ill defined activities education, health, and social services real estate and business services other collective, social, and personal transportation, storage, and communication accommodation and food services material extraction and processing building construction trade and repair
? 61.6 k
30.3 k
20.3 k
15.6 k
14.4 k
12.1 k
7.9 k
3.8 k
.8 k
?? .3 k
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Backpropagation + Abstract Cellular Automata
Neural Networks + Sensitivity Analysis T-SQL + BBMeta + Processing 2.0
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carrot
stick
supervised learning in neural networks An artificial neural network is an approach to processing information in a manner intended to mimic the way biological neurons process information. In backpropagation, a desired output is known and the neural network learns from using multiple inputs to guess the known output. If the neural net guesses correctly, it receives a reward and the connection that led to the correct guess increases in strength. If it guesses incorrectly, it receives punishment and the connection that guessed incorrectly decreases in strength.
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1300029130002900100100022464004047601767806310300500101 63 111 V4002, “TIPO DE ESPECIE� 11- Casa 12- Casa de vila ou em condominio 13- Apartamento 14- Habitacao em casa de comodos, cortico ou cabeca de porco 15- Oca ou maloca 51- Tenda ou barraca 52- Dentro de estabelecimento 53- Outro (vagao, trailer, gruta, etc) 61- Asilo, orfanato e similares com morador 62- Hotel, pensao e similares com morador 63- Alojamento de trabalhadores com morador 64- Penitencieria, presidio ou casa de detenco com morador 65- Outro com morador
104010030131 20111111121122 2220412000096800001898040002420000004745130000000000000000000000000000 V4002, “HOUSING TYPE” 11 - House 12 - Town house or condo 13 - Apartment 14 - Residence: tenement or pig’s head 15 - Oca or longhouse (hut) 51 - tent or shack 52 - Within establishment (impromptu infill) 53 - Other (wagon, trailer, cave, etc.) 61 - Asylum, an orphanage and with similar resident 62 - Hotel, pension and similar with resident 63 - Accommodation for workers with resident 64 - Penitentiary, prison or home detention with resident 65 - Another resident with
error guessing housing type
household census data
A neural network was trained using a backpropogation algorithm on 2010 household census data. The four categories used to tell the neural network that it was accurately guessing informal housing were huts, tents or shacks, impromptu infill, and other (including wagon, trailer, cave, etc.). Additionally, housing that was occupied in an improvised manner, such as the squatting of a shop, factory, or space not zoned for housing, such as a car, trailer, barn, or cask was also considered as a target output for the neural network.
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37 total household census data variables Using the BBMeta software package, a neural network was established with 3 input nodes (census data), 6 intermediary nodes (hidden layer), and 1 output node (guess of formal or informal). The data was split into a training set of 6,000 households and a test set of over 40,000 households. Having tested the neural network, weights of each variable were output in order of greatest to smallest in order to determine how each of the census household level variables contribute to the accurate prediction of whether a household is formal or informal.
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1 Existence of Radio 2 Brazilian Region 3 Existence of Plumbing 4 Existence of Television 5 Number of Bedrooms 6 Microregion Code
19 Mesoregion Code 20 Urban or Rural 21 Existence of Land Phone 22 Existence of Computer 23 Garbage Destination 24 Amount of Rent Paid
7 Existence of Refrigerator 8 District Code 9 Existence of Cell Phone 10 Geographic Region 11 Subdistrict Code 12 Exterior Materlals
25 Existence of Electricity 26 Metropolitan Code 27 Out houses 28 Minimum wage 29 Water Supply - Type 30 Waste Removal - Type
13 Number of Comodos 14 Washing Machine 15 Exclusive Bathrooms 16 Type of Housing 17 Electricity Metering 18 Dormitory Density
31 Type of Unit 32 Control 33 Municipal Code 34 Condition of Occupancy 35 Residential Bathroom Density 36 Existence of Motorcycle
Lighter green indicates variables that were purely political, geographic demarcations. These variables were removed from further analysis since they contributed no unique data beyond the bounds of the Amazonas region.
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5 number of informal indicator variables Using the variable weights discovered in the prior analysis, a cellular automata model was created and run to simulate informal settlement activity in the abstract case. Based on an initial pass of the abstract case, the simulation to the right show that a very crude rule set can yield interpretable results. For instance, in the case to the right, cells were randomized to have an initial value of dead, formal, or informal, and an informality indicator based on variable weights. Once the sum of the weights exceeded a certain threshold, the cell became informal.
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This case highlights an instance of an informal cell that starts around surrounded by formal cells, becomes fractured as the formal cells crowd out some of the informal cells (we might call this gentrification), splits into four strips of informal cells sitting in isolation in a larger field, and then final shrinks down and consolidates into a stable form of four cells in a square (strength in numbers, but far enough away from formal cells to not become crowded out again) over a span of 400 iterations.
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Belem, Para, Brazil Simulation
Cellular Automata MS Excel + QGIS + Processing 2.0 + Rhino 4.0
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mouseclick
wvarinf t t=0
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informal growth explosion post 100th iteration
Because the initial rule set is not grounded in physical space and at this point was purely hypothetical, it did not take into account certain potentially crucial simulation variables, such as proximity to an unoccupied edge, proximity to waterways, and real world barriers such as roads. The abstract case implies that given no fixed nodes and a random initial state, informal growth could be logarithmic. If one considers the IIRSA plan, intended to connect all of South America with large infrastructural projects, this is not an unreasonable starting point.
slow informal growth through 100 iterations
summation of informal indicators
Using QGIS and satellite imagery from Google Earth, data points were extracted from a neighborhood in Belem, Brazil along the R. dos Mundurucus and Pass. Boa Esperanca. This informal area was chosen due to its proximity to a river, formal housing, a large fixed parcel with low likelihood of being converted to housing in the form of a cemetery, and selection by another student for a studio project with the intention to ultimately incorporate the students proposed studio project into simulation analysis.
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6 rules used for Belem Game of Life
2) A well established informal with two formal neighbors, and a with a radio, tv, low bed density, ing, and a refrigerator becomes
parcel history plumbformal.
3) Edge parcels (between a road and a river, for instance) with three informal neighbors become occupied and informal.
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formal, mature formal, young informal, mature informal, young e edge undeveloped
parcel components
1) Young “living� parcels that have fewer than two neighbors don’t have the community support they need to survive and thrive, so in the subsequent round of the game, they perish.
x coordinate y coordinate type (road, edge, etc.) current state (developed, not) previous state (developed, not) radio tv bed density refrigerator plumbing age
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4) Young, developed parcels that have more than 3 formal neighbors become undeveloped because of overcrowding. 5) Young undeveloped parcels with more than 2 informal neighbors become informal.
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6) Undeveloped parcels with exactly 3 formal neighbors become developed since they have enough community support to thrive as formal parcels.
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selected stasis patterns from 1011 rounds 1) Formal Split: Two formal parcels surround by 6 informal parcels 2) Peaking into the Courtyard: 11 formal parcels with an undeveloped courtyard and an informal cell in a corner c) The Corridor: A long informal corridor surrounded by formality 3) 2x4: Two pairs of mature and young formal cells next four pairs of mature informal cells
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Quinta Monroy
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Street Vendors
5) Impenetrable Corridor: Alternating young and mature formal cells flanking undeveloped space with no informal entry g
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Paraisopolis e
4) Commingled: Two young formal parcels, one mature formal parcel, two informal parcels, and four undeveloped parcels
g) Though it is not a stasis pattern, it is worth noting that by having omitted just a few road cells from the northeast area of examination, informal growth became completely unbounded.
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Next Steps in Analyzing Informal Networks
Cellular Automata Neural Network + 3D Data Python + QGIS + Rhino 4.0
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11 parameters currently used per parcel Currently, the variables used for accurately identifying informal parcels are identified statically outside of the geo-spatial context. Census data is churned through a neural network connected to output from a database environment. In a more idealized iteration of this research, each parcel contains a neural network that learns dynamically during the cellular automata simulation, updating variable weights dynamically based on inputs learned from its surrounding context.
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1300029130002900100100022464004047601767806310300500101 63 111
104010030131 20111111121122 2220412000096800001898040002420000004745130000000000000000000000000000 Additionally, if Google Street View is available for the area under consideration, construction material data can be extracted from each elevation and tied to each planometric parcel. This information is currently missing from the analysis even though construction materials and techniques used are often the most obvious visual indicators of informal settlements (though they may or may not indicate income level, health, or other variables). Elevation data can also give indication as to whether or not sectional informality exists.
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2011 last available USGS viewer image Finally, the component of real settlement change over time is currently missing. Without the aspect of historical data, the “leave one out� approach must be taken in order to be able to test if a model can accurately guess if a parcel is informal or not. In other words, if we have six known and understood blocks, can we train a model to accurately guess what the sixth block is if we feed the model the other 5 blocks. This approach can be used for a single point in time, but in order to accurately model growth, historic parcel 1922
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This can be done by converting historic maps into .SHP files and attempting to extrapolate parcel level data for historic maps, such as the 1922 map to the left, based on photo archives and municipal tax records (for instance, the Sanborn map collection, a series of maps used for fire insurance, could serve as a useful proxy to property values and building materials).
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conclusions The combination of cellular automata and neural networks can be used to attempt to simulate informal settlement behaviour and extract explanatory rule sets. This method aids in the debunking of the aesthetization of informality and moves the study of the informal away from a formalinformal discrete structure to a continuous informal gradient paradigm. Potentially, such a paradigm could be leveraged to implement efficiencies found in informal networks that formal networks prohibit.
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Benjamen Prager | Spring 2013 | Lally School of Management + Technology | School of Architecture | Rensselaer Polytechnic Institute
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references Almeida, C.M., Gleriani, J.M., Castejon, E.F., and Soares-Filhoy, B.S. “Using neural networks and cellular automata for modeling intra-urban land use dynamics.” International Journal of Geographical Information Science. Volume, Date, and Page Number unlisted (still searching). Batty, M., Coucleus, H. and Eichen, M., 1997, Urban systems as cellular automata. Environmental and Planning B, 24, 175–192. Johnson, Steven. Emergence: The Connected Lives of Ants, Brains, Cities, and Software. New York City, Scribner, 2004. Print. Li, Xia and Yeh, Anthony Gar-on. “Neural-networks based cellular automata for simulating multiple land use changes using GIS.” International Journal of Geographical Information Science. Volume 16, No.4, 2002: 323-343. Print. Pijanowski, Bryan, Brown, Daniel, Shellito, Bradley, and Manik, Guarav. “Using neural networks and GIS to forecast land use changes: a Land Transformation Model.” Computers, Environment and Urban Systems. Article in Press (uncorrected proof available for download at www.elsevier.com/locate/compenvurbsys). Wang, F., 1994, Use of artificial neural networks in geographical information system for agricultural land assessment. Environment and Planning A, 26, 265–284. Ward, D.P., Murray, A.T. and Phinn, S.R., 1999, An optimized cellular automata approach for sustainable urban development in rapidly urbanizing regions. Available at:www.geovista.psu. edu/sites/geocomp99/Gc99/025/gc0 _ 25.htm (accessed 26 October 2012). Wolfram, Stephen. A New Kind of Science. Champaign, IL, Wolfram Media, 2002,
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web links http://eijournal.com/2012/collective-sensing-sheds-light-on-urban-landscapes http://www.earthzine.org/2011/08/16/population-estimates-in-informal-settlements-using-object-based-image-analysis-and-3d-modeling/ http://www.fastcoexist.com/1679799/new-tech-lets-an-army-of-informal-recyclers-collect-brazilian-waste used for second page informal labor force data: http://www.ilo.org/wcmsp5/groups/public/---dgreports/---stat/documents/presentation/wcms_157467.pdf http://asterweb.jpl.nasa.gov/images/GDEM-10km-BW.png http://www.nationmaster.com/graph/eco_infe_ co-economy-informal http://www.forbes.com/sites/kenrapoza/2012/09/25/in-brazil-the-poor-get-richer-faster/ http://glovis.usgs.gov/ http://img0.etsystatic.com/000/0/5446290/il_fullxfull.185726364.jpg
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