Gis update2010 som-legibility

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Presented at http://www.geo.ed.ac.uk/gisupdate/ in 2010

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  • Indonesia
  • TOWERING GROWTH
    Modern office blocks rise amid squalid neighborhoods lacking basic sanitation in the capital of the world's fourth most populous—and 87 percent Muslim—nation. Jakarta reflects Indonesia's vast resources, but also its astonishing problems, including: poverty, overpopulation (expected to increase 12-fold from 1950 to 2015), horrendous traffic, stifling pollution, and endemic corruption.
  • Jakarta, strip
    earch revisits the work of urban designer Kevin Lynch, in particular his concepts of the ‘legible city’, ‘urban imageability’, and ‘cognitive mapping’. This proposal develops that preliminary investigation by drawing on three distinctive intellectual contexts: first, architecture, urban design and landscape theory; second, geography and Southeast Asian studies; and third, GIS science, public participatory GIS and geospatial hypermedia.
    The project is sited in Jakarta, and draws on ethnographic research conducted by a group of researchers from the city.  The research takes the form of video interviews designed to elicit the details of the interviewee’s everyday engagement with the city.  This explores the routes they use, the territories they are familiar with, and their understanding of the city in general.  The interview and video footage is enlarged upon by a mental map.  These mental maps form the basis of our research, helping to develop a language appropriate to this city, and it’s desa-kota (peri-rural) condition which can resist traditional forms of mapping, drawing, and notation.
    The significance of this research is three-fold. First, the desa-kota landscape itself deserves sustained academic attention as it represents an emergent mode of settlement that supports some of the largest population concentrations in the world. Research on this condition has the potential to contribute to (theoretical and policy) debates about the fortunes of this mode of settlement in Jakarta and Southeast Asia, and to link these debates to wider discussions on landscape urbanism, which are currently oriented almost exclusively towards European and American exemplars. Second, there has been, to date, little work on the ways in which visual media and representational systems – conventionally understood to be mutely instrumental – impact upon the design, planning and management of extended metropolitan regions.
    RESEARCH QUESTIONS
    1.How might international discussions on urban legibility, landscape urbanism, and the late capitalist city inform urban design and development in desa-kota zones?
    2.What are the different visual systems by which desa-kota zones are represented and to what ends are they put: planning, property speculation, navigation, orientation?
    3.How are desa-kota zones represented within the GIS-supported representational logics in use in the state urban and regional planning system?
    4.What are the characteristics of the emergent, less formal representations of desa-kota zones that circulate in the popular media, NGO and community circles?
    5.How do such representations relate to official representations: do they correspond, overlap, contradict, mix, or convolute?
    6.What possibilities exist for the emergence of the less formal cultures of legibility in desa-kota zones, and for their interaction with official representations of desa-kota zones, without one subsuming the other?
    7.How can new web-based, interactive geographic information technologies be used to explore these possibilities?
  • 4.What are the characteristics of the emergent, less formal representations of desa-kota zones that circulate in the popular media, NGO and community circles?
    5.How do such representations relate to official representations: do they correspond, overlap, contradict, mix, or convolute?
    TOWERING GROWTH
    Modern office blocks rise amid squalid neighborhoods lacking basic sanitation in the capital of the world's fourth most populous—and 87 percent Muslim—nation. Jakarta reflects Indonesia's vast resources, but also its astonishing problems, including: poverty, overpopulation (expected to increase 12-fold from 1950 to 2015), horrendous traffic, stifling pollution, and endemic corruption.
  • http://www.nerc.ac.uk/research/programmes/espa/documents/Final%20Report%20Desakota%20Part%20II%20A%20Reinterpreting%20Urban%20Rural%20continuum.pdf
  • Field work result example 1
  • Source: http://www-vis.lbl.gov/Events/SC07/Drosophila/3DParallelCoordinates.png
    View as a parallel coordinate plot, in which each plane represents a specific question, and each ‘thread’ through those planes represents a particular individual’s response.
    BUT – what are the patterns in this data – are there meaningful ways by which we can group these responses? Are there outliers? Can we discern ‘new species’ or groups not previously identified in the Desakota?
  • artificial neural network
    ANN - system loosely modelled on the human brain. An attempt to simulate the multiple layers of simple processing elements called neurons. Each neuron is linked to certain of its neighbours with varying coefficients of connectivity that represent the strengths of these connections. Learning is accomplished by adjusting these strengths to cause the overall network to output appropriate results.
    Trial and error: uses process of learning, iterative nature. Similarity to biological neurons.
    Input - set of n-dimensional observations. Output = network of nodes/akin to raster model with references to input data. Input vectors train the neuron grid so that topological relationships among input observations are preserved.
    Complexity - formulas!
    The SOM is a new, effective software tool for the visualization of high-dimensional data. It converts complex, nonlinear statistical relationships between high-dimensional data items into simple geometric relationships on a low-dimensional display. As it thereby compresses information while preserving the most important topological and metric relationships of the primary data items on the display, it may also be thought to produce some kind of abstractions. These two aspects, visualization and abstraction, can be utilized in a number of ways in complex tasks such as process analysis, machine perception, control, and communication.
    The SOM algorithm computes the models so that they optimally describe the domain of (discrete or continuously distributed) observations.
  • Code junkies who want to make their own SOM
    http://www.ai-junkie.com/ann/som/som1.html
    World poverty map
    http://www.cis.hut.fi/research/som-research/worldmap.html
  • Analaysis of questionnaires – generation of component planes
  • Ethnicity: Betawi, Javanese, Sudanese, Other
    Betawi people - local inhabitant of Jakarta - descendants of the people living around Batavia (the colonial name for Jakarta)
  • km to doctor, km to school, km to bank, food and clothes
  • km to doctor, km to school, km to bank, food and clothes
  • Medium and large gated communities
    Land tenure, length of time lived in community
  • Medium and large gated communities
    Land tenure, length of time lived in community
  • Analaysis of questionnaires – generation of component planes
  • Principal component analysis (PCA)is used to find patterns amongst high dimensional data (Maindonald and Braun 2010; Shlens 2005). The technique involves calculating eigenvectors from the covariance matrix of the data. It is an exploratory data analysis technique that involves a mathematical procedure that transforms a number of possibly correlated variables into a smaller number of uncorrelated variables called principal components. PCA can be used to measure the variability in response among those questioned, help us to discern groupings, and how they are constituted. It can also help us to assess whether the groups are opposed. Maindonald, J and Braun, W.J. 2010 Data Analysis and Graphics Using R: An Example-based Approach. Cambridge University Press. Third edition. Shlens. J. 2005. A Tutorial on Principal Component Analysis. Copy retrieved [04-06-2010] from: http://www.cs.cmu.edu/~elaw/papers/pca.pdf But I need some words that describe in lay terms what the figure shows us: In your figure, are those variables furthest from the centre, those with the largest eigenvectors, and thus best able to account for the patterns discernible in the data? When we see opposed variables, (eg Desa and Kampung on one side, and Medium gated communities on the other), are we saying that 'those in gated communities are quite distinct from both the small rural village (Kampung) and the rapidly urbanising rural areas (Desakota)' Are we also saying that those in the Desakota are also a long way from various services (Banks, schools, shops)? Is this because the services have not yet arrived in the Desakota? What are we able to say about the proximity of 'long lived in the city and having Betawi ethnicity? I suppose what I need are some annotations of the PCA map that I can convey to the audience on Friday.
  • The output plane, cells sized according to the number of respondents in that group.
  • Jakarta, strip
    earch revisits the work of urban designer Kevin Lynch, in particular his concepts of the ‘legible city’, ‘urban imageability’, and ‘cognitive mapping’. This proposal develops that preliminary investigation by drawing on three distinctive intellectual contexts: first, architecture, urban design and landscape theory; second, geography and Southeast Asian studies; and third, GIS science, public participatory GIS and geospatial hypermedia.
    The project is sited in Jakarta, and draws on ethnographic research conducted by a group of researchers from the city.  The research takes the form of video interviews designed to elicit the details of the interviewee’s everyday engagement with the city.  This explores the routes they use, the territories they are familiar with, and their understanding of the city in general.  The interview and video footage is enlarged upon by a mental map.  These mental maps form the basis of our research, helping to develop a language appropriate to this city, and it’s desa-kota (peri-rural) condition which can resist traditional forms of mapping, drawing, and notation.
    The significance of this research is three-fold. First, the desa-kota landscape itself deserves sustained academic attention as it represents an emergent mode of settlement that supports some of the largest population concentrations in the world. Research on this condition has the potential to contribute to (theoretical and policy) debates about the fortunes of this mode of settlement in Jakarta and Southeast Asia, and to link these debates to wider discussions on landscape urbanism, which are currently oriented almost exclusively towards European and American exemplars. Second, there has been, to date, little work on the ways in which visual media and representational systems – conventionally understood to be mutely instrumental – impact upon the design, planning and management of extended metropolitan regions.
    RESEARCH QUESTIONS
    1.How might international discussions on urban legibility, landscape urbanism, and the late capitalist city inform urban design and development in desa-kota zones?
    2.What are the different visual systems by which desa-kota zones are represented and to what ends are they put: planning, property speculation, navigation, orientation?
    3.How are desa-kota zones represented within the GIS-supported representational logics in use in the state urban and regional planning system?
    4.What are the characteristics of the emergent, less formal representations of desa-kota zones that circulate in the popular media, NGO and community circles?
    5.How do such representations relate to official representations: do they correspond, overlap, contradict, mix, or convolute?
    6.What possibilities exist for the emergence of the less formal cultures of legibility in desa-kota zones, and for their interaction with official representations of desa-kota zones, without one subsuming the other?
    7.How can new web-based, interactive geographic information technologies be used to explore these possibilities?
  • artificial neural network
    ANN - system loosely modelled on the human brain. An attempt to simulate the multiple layers of simple processing elements called neurons. Each neuron is linked to certain of its neighbours with varying coefficients of connectivity that represent the strengths of these connections. Learning is accomplished by adjusting these strengths to cause the overall network to output appropriate results.
    Trial and error: uses process of learning, iterative nature. Similarity to biological neurons.
    Input - set of n-dimensional observations. Output = network of nodes/akin to raster model with references to input data. Input vectors train the neuron grid so that topological relationships among input observations are preserved.
    Complexity - formulas!
    The SOM is a new, effective software tool for the visualization of high-dimensional data. It converts complex, nonlinear statistical relationships between high-dimensional data items into simple geometric relationships on a low-dimensional display. As it thereby compresses information while preserving the most important topological and metric relationships of the primary data items on the display, it may also be thought to produce some kind of abstractions. These two aspects, visualization and abstraction, can be utilized in a number of ways in complex tasks such as process analysis, machine perception, control, and communication.
    The SOM algorithm computes the models so that they optimally describe the domain of (discrete or continuously distributed) observations.
  • Gis update2010 som-legibility

    1. 1. GISUpdate2010 1 Understanding ‘Desakota’: The Case Study of Jakarta Vlad Tanasescu , William Mackaness, Stephen Cairns, Ray Lucas http://ddm.caad.ed.ac.uk/groups/jakarta/
    2. 2. GISUpdate2010 2 Outline • Jakarta • Desakota • Maps, Video & Questionnaire • Self Organising Maps • Interpretation • Conclusion
    3. 3. GISUpdate2010 3
    4. 4. GISUpdate2010 4 Jakarta • Global City of the South - Fourth most populous city in the world: 23 m • Elevation 4m • 87% Muslim • Poverty, congestion, horrendous traffic, stifling pollution, endemic corruption
    5. 5. GISUpdate2010 5
    6. 6. GISUpdate2010 6 Focus • The Image of The City by Kevin Lynch: • the legibility of the city - "...the ease with which its parts may be recognised and can be organised into a coherent pattern." • Our focus: the Desakota • Longitudinal study: ‘a strip’ • Research questions..perception, opportunity, impediment..
    7. 7. GISUpdate2010 7 Desakota • ‘village – town’ • Sociological, ecological, political, informal space…. • Differentially stressed…eg access to services • Rapid displacement, migration, commercialisation commercial/ subsistence agriculture • mixed household economy - straddles the periurban
    8. 8. GISUpdate2010 8 The Urban
    9. 9. GISUpdate2010 9 The Rural
    10. 10. GISUpdate2010 10 Kampung, Desa, & Gated Communities • Kampung – urban settlement • Desa – rural village • Gated Communities
    11. 11. GISUpdate2010 11
    12. 12. GISUpdate2010 12
    13. 13. GISUpdate2010 13 Methodology • Architectural students • Interviews • Cognitive Maps • Questionnaires
    14. 14. GISUpdate2010 14 Methodology
    15. 15. GISUpdate2010 15
    16. 16. GISUpdate2010 16 Questionnaire • Ethnicity • Community • Land ownership • Education • Access to services • Social isolation • Mobility • …. • 1000 • 39 variables • Data preparation -Normalisation
    17. 17. GISUpdate2010 17 Tenure Communit y Occupation City_visit Access to service Ethnicity Mobility Education
    18. 18. GISUpdate2010 18 Self Organising Map • “a similarity graph, and a clustering diagram, too. Its computation is a nonparametric, recursive regression process” (Kohonen, 2000) • Form of artificial neural network. • Computationally intensive. • unsupervised learning algorithm (neural networks)
    19. 19. GISUpdate2010 19 Purpose • Represent high-dimensional data in a low- dimensional form without loosing any of the 'essence' of the data. • Organise data on the basis of similarity by putting entities geometrically close to each other.
    20. 20. GISUpdate2010 20 SOM
    21. 21. GISUpdate2010 21
    22. 22. GISUpdate2010 22
    23. 23. GISUpdate2010 23 Worldpovertymap http://www.cis.hut.fi/research/som-research/worldmap.html
    24. 24. GISUpdate2010 24
    25. 25. GISUpdate2010 25 A Component planes Ethnicity: Betawi, Javanese, Sudanese, Other
    26. 26. GISUpdate2010 26 Isolation
    27. 27. GISUpdate2010 27 Isolation… both Desa and Kampung
    28. 28. GISUpdate2010 28 Recent development City resident: time Land ownership Gated medium large Communities:
    29. 29. GISUpdate2010 29 Melting Pot Kampung Desa Betawi Sudanese Javanese Other
    30. 30. GISUpdate2010 30
    31. 31. GISUpdate2010 31 Principle Component Analysis • Patterns among high dimensional data • Calculates eigenvectors from covariance matrices • Measure variability in responses, discern and account for groupings, identify opposing groups
    32. 32. GISUpdate2010 32
    33. 33. GISUpdate2010 33
    34. 34. GISUpdate2010 34
    35. 35. GISUpdate2010 35
    36. 36. GISUpdate2010 36 Conclusion • Exploring the Desakota • Combination of EDA techniques • ‘linking’ with video • Further Develop methodology • Apply in China and India – global cities of the south..

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