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Pittsburgh Pennsylvania, USA 2016-2014

This page contains 4 different initiatives, all of which are intrinsically connected, but each with a different particular goal.  In brief, I am investigating how to distributively transform cities through other people and organizations by changing their tools.  Changing the tools requires a deep investigation into the underlying values and philosophies of our technologies, a consideration of how humans assemble themselves in relation to technologies, and a consideration of how these factors will unfold in coming decades.

Hybrid spatial Infrastructures for Urban Robotics

Geographically located volumetric policy zones (lat/long/elevation) can be transmitted to UAVs to determine flight patterns and usage rights

Geographically located volumetric policy zones (lat/long/elevation) can be transmitted to UAVs to determine flight patterns and usage rights

The practice and education of Urban Planning has failed to assert itself as an influential entity within the development of advanced technologies in the 21st century.  Presently, the entire planning discourse is reactionary to robotics, information systems, and artificial intelligence. Questions abound such as "how will autonomous vehicles impact transportation or how will drones impact urban design?"  Years back, I asked the same questions, "how will robotic warfare inform post-war reconstruction?"

In contrast, my research concerning urban robotics is proactive. How do these new machines and sensor systems work? What can I do with them? How can I change them? By understanding the technology and anthropologically observing the relationship between humans and technology, it is possible, to design new relationships between machines and social systems. Although the bulk of my work concerns sense-making, my first independent foray into this territory concerns the use of urban zoning codes to control UAVs. You can read about the work in detail at the Humanitarian Space.  This work has been appropriated by NASA for strategic UV infrastructure planning for the year 2035.

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Problem: Sense-Making, Mapping, and Urban Analysis

Cognitive sciences have found that the process of sense-making does share some universality across cultures, languages, and geographies. In particular, the role of card sorting to capture cognitive models of poorly structured problems thus appears the most fluid methodology, and therefore informs much of how I approach new technologies.

My standard methods to engage complex problems integrate grounded theory, ethnography, computation, and design driven sense-making.  The core question of my current research is simple: can we develop more robust methods to understand and complex urban environments? 

Within this problem, I am motivated by a desire to find new ways to validate design methods and outcomes as design too often lacks sufficient validation methods aside from reliability via iterative deduction. Furthermore, my goal is to identify possible ways to operationalize post-normal science within computational methods, so as to detach from the inherited legacy of cybernetics within the creation of new urban futures.

In other words, if the technologies for urban research and design have particular histories distinct from design - histories rooted in military goals and the cold war-are these tools in fact best configured for design?  How have these histories have shaped look, feel, operation, and deployment or urban research and design technologies (GIS, AutoCAD, PointClouds), and how has this impacted the built and social environment?

In contrast, if one attempted to drop the inherited conventions that shape GIS (for example), and built such technologies from an alternative theoretical basis, to do something similar (to understand the layered spatial implications of complex human environments), what would that tool do? How would it work? How would it feel? What is the consequence for architecture and urban planning?


Applied Machine Learning for Rapid Mapping of Urban Systems

Machine Learning of Phone Images extracted 3 coherent data layers: Public Health, Security, and Damages. Network Analysis is applied with each theme to build network ties between similar points within a 1/3 of a mile, as determined by urban walkability (not as the crow flies).  The result is a rapid and emergent identification of neighborhoods.

Problem

It is difficult to map locations of limited infrastructure or increased danger. Satellite, via community platforms such as OpenStreetMap, is the primary method. Ground truth data collection, however is difficult and expensive. My repeated mapping of Mogadishu, for example, required dozens of people, technical support, and months of work. So if a disaster or act of social violence necessitates a rapid 'big picture' understanding of what is happening and where - how can we do this faster? More importantly, how can we do this in a way that is less culturally subjective and universal?

Goal
In my current design research, I seek new methods to quickly understand complex environments. One output of this research is the fusion of machine learning and social network analysis with urban photography. The objective of this work is to move away from the rigidity of discrete computational data within complex environments (which frequently contains deep cultural bias) and to utilize the fluid and subjective content of photography as data within urban computation.

Result
This work has generated an urban systems map that captures the shape and composition of neighborhoods relative to the variety of source data (see above). It also captures the non-geographic organizational composition of urban systems - such as a measure of robustness. Possible applications include rapid urban assessments, culturally relevant indicator determination for economic or health appraisals, and rapid infrastructure mapping for emergency and humanitarian relief.

Methodology
3,000 personally collected images from Mogadishu were processed via CAFFE. The machine learning classifications were not culturally tuned to the content of the images, and that work remains for the next iteration, but are derived via a random and generic classifier using the MIT CSAIL places library. This work was assisted by Geoffrey Morgan.

Derived image descriptions from the Machine Learning included phrases such as "security, medical, house, hospital." To validate the accuracy of these keywords, samples were randomly compared against on-the-ground urban assessment data I collected in Mogadishu in 2013. The resulting keyword index was then consolidated by keyword co-occurence (see grey/black visualization) and the three largest concentrations of tagged images were extracted (1,816 images).

A Manhatten Analysis was applied to this new index, essentially clustered similar data points by walkability to capture the "neighborhood footprint" of relationships. The result is a map that can be instantly generated from unstructured image data, relaying the distribution, layering, and user interaction patterns of urban infrastructures.

10,000 mobile phone images of Mogadishu

10,000 mobile phone images of Mogadishu

Machine learning applied to images can generate keyword descriptions of images. Example image above shows MIT CAFFE deep learning framework applied to image of Mogadishu to create tags

Machine learning applied to images can generate keyword descriptions of images. Example image above shows MIT CAFFE deep learning framework applied to image of Mogadishu to create tags

Machine Learning generates 20 tag descriptions per image. Tags are clustered into groups by co-occurrence to collect emergent data layers

Machine Learning generates 20 tag descriptions per image. Tags are clustered into groups by co-occurrence to collect emergent data layers

Traditional POI Maps of Mogadishu lacks comparative insight to ML output (top)

Traditional POI Maps of Mogadishu lacks comparative insight to ML output (top)

Photo forensics For human/environment Interaction

The image above contains an architectural reconstruction and an statistical sample of human movement within the space, built from 2 sets of images.  Each set contained a 3 second photo burst from an iPhone 5s - generating 30 images per burst.

The image above contains an architectural reconstruction and an statistical sample of human movement within the space, built from 2 sets of images.  Each set contained a 3 second photo burst from an iPhone 5s - generating 30 images per burst.

30 iPhone images, shot from 2 corners of my studio

Aerial Reconstruction from collected images, created by algorithmically tracing light patterns across the floor

Aerial Reconstruction from collected images, created by algorithmically tracing light patterns across the floor

Introduction
I have long admired the work of the Design Methods Movements with individuals such as Buckminster Fuller, Herbert Simon, and Christopher Alexander. Likewise, the efforts of the second Methods movement, to pursue a more holistic approach to design with a focus on behavior/environment studies greatly shaped my work within data collection and ethnographic research.  

In particular, John Zeisal's work on post-occupancy analysis dramatically informed my practice, as recorded in this essay about my use of social artifacts for rapid systems appraisals in Ethiopia. Inspired to take this approach further, this studio experiment concerned the application of photography to capture an environment in use for forensic and spatial analysis. 

Exploratory Problem
GIS systems continue to rely upon discrete data structures to understand complex social and spatial relationships.  I sought to discover how the experimental application of computer vision technologies for problems such as autonomous automotive navigation could generate new ways of understanding architectural and urban environments.

Results
With as little as 60 images, it was possible to model a contained environment and document human movement within the architectural space. While the outcome is moderately novel, a greater opportunity is now possible in the use of the heat map data as statistical sample for behavior/environment simulations with agent based modeling. 

Methodology
In the images above, a total of 60 images were collected from two corners of the room using an iPhone 5s. The photos documented the traffic pattern of an individual moving through the room. The first step was to calibrate the images into a planar reconstruction of the room.  Object identification and tracking was applied to the human subject and the movement was documented as a heat map.


In the images below. the planar reconstructions created from both points of view were stitched together by tracking the patterns of light diffusion across the floor boards. The human tracking heat maps were also stitched together by matching pixels colors and superimposed on top of the newly constructed environment

 

 

GIS Cloud Platform Driven by Machine assisted text mining

Hi-Fidelity Prototype of Text Processing Window for GIS Analytics

Above: The Industry Standard GIS UI/UX

Problem
Geographic Information Systems (GIS) are used in many industries to understand the complex spatial relationships of economics, society, and the environment. They are part of the standard toolkit of any architect, urban planner, and urban designer, to inform the design of new urban spaces and environmental interventions. Today's GIS information systems maintain an antiquated workflow, interface design, and data requirements.

If GIS was approached from a new point of view - not that of the 'analyst' - but from the point of view of the analyzed subject, what would be the result?  If the platform did not maintain traditional data requirements such as shape files but could leverage unstructured information such as images and text, how would that work?

This work embodies an attempt to change the entire model of GIS, thus to transform the design process that shapes the built environment.

Currently Deployed Build (Above) Consistent with Schema as an iterative step toward ultimate vision, yet also as a complete user experience

Currently Deployed Build (Above) Consistent with Schema as an iterative step toward ultimate vision, yet also as a complete user experience

Result
The Symkala GIS platform is a radical new take on GIS technologies. Inspired by batch processing platforms for multimedia such as Apple Aperture and Adobe Lightroom, Symkala is designed around the need for teams to conduct phased workflows of data preparation, synthesis, visualization, and simulation.  The image below is a screen shot of the active build of Symkala, built on Amazon AWS from Python and Java. It includes the use of machine learning for rapid urban systems mapping (Research Project I) and with linguistic analysis can extract entity to geography relationships from text data.  It then identifies the location coordinates of the geographic place.
 

UX / UI and Development Methodology (Ongoing) 

Mockup (Above) for Current UX/UI Schema

Mockup (Above) for Current UX/UI Schema

In tandem with previous research effort concerning machine learning, mapping, and forensics, I have additionally conducted human centered research with urban developers, humanitarian workers, government employees, intelligence experts, and geographers.

Channeling the design research into building the platform  massive challenge requiring a bigger team.  Geoffrey Morgan supplied computational analytical abilities, Will Milner has led the core platform coding, Rachel Chang and Jeffrey Houng assisted in rapid prototyping and UI Design.  The entire process has been conducted with zero funding. 

ur team methods include site-based user research, qualitative interviews, design workshops, and speed dating with paper prototypes. Using an agile development process, software is constructed via biweekly sprints (captured in grid of images, click to expand).

Hi-Fidelity UI design mockups (Below) document ideal design output for login, archive view (data processing), card sort (synthesis and sense making) and DataVis.