This document summarizes the creation of several geospatial visualization tools to represent trauma injury data in Cape Town, South Africa. Photographs were taken to recreate Google Street View panoramas of roads with high injury rates. The photos were stitched together using Hugin to create panoramic views. Google SketchUp was used to build 3D models of intersections based on the panoramas. Google Earth was used to create bar graphs of injury data for different suburbs. Balloons in Google Earth displayed the graphs and additional information about each location.

1. GSV proves to be
both problematic
and helpful. Spine
Road (first image)
was captured at
different times.
There is a time lag when the two separate sides of the street
were stitched together, depicting two different scenarios. On the
other hand, the risk factor leading to a possible injury is clearly
captured on Lansdowne Road Extension (second image).
In many parts of the world, Google Street View is not available. Just because it
is not available, it does not mean we can’t fake it. (This project began before
Google’s release of Street View in South Africa with the commencement of the
World Cup). Through the collaboration with Prestige Makanga, photographs
were taken of the suburbs with the highest aggregate trauma injury records, in
particular, a few main roads, to get a sense of the physical environment in each
area and the environmental risk factors involved. Focus was placed on Spine
Road, a main road in one of the most injury prone suburb in Cape Town,
Khayelitsha. The important part of having a local person be involved in the
process of the geovisualizations, is the local knowledge that can be transmitted
to the remote mashup creators (i.e. identifying shebeens, illegal alcohol
establishments).
Visualizing built environments and injury
in low resource settings
After the photographs were taken, Hugin, a free open source stitching program,
was used to create panoramas of the roads. Hugin is a great program that
does not have a steep learning curve, and is easy to understand. It does take
some trial and error, depending on the photos being stitched together. It has an
online help center that details each tab (Assistant, Images, Camera and Lens,
Crop, Control Points, Optimizer, Exposure, Stitcher), the tools available,
development and general information.
Interoperability: What is the application’s ability to work with other existing
programs?
Modifiability and collaborative work: What is the level of ease of adaption,
and are multiple persons able to work on the application?
Cost: Generally looked at applications that were free of charge, especially
significant when dealing with low-resource communities
Interactivity: Do developers and end users have a good exchange of
information? Are the user interfaces and lay out understandable? Is the
data communicated well?
Quality: How efficient/effective are the application user interfaces used?
Realism: How realistic or abstract will the application be?
The geovisualization criteria used in building our tools:
Introduction
SII-PIV 54 Spatial and Environmental Injury Surveillance, based on Cape Town,
South Africa. So what is the project about? It is about seeing if we can use the
geospatial web to work with trauma surgeons, nurses, health officials, to
represent trauma injury data in a useful way. It is very informal due to working
with coarse data, poor and dangerous neighbourhoods. We focus on ‘one on
one’ interactions between the user and the application. And lastly, we hope that
these tools will be practical in the health world.
A set of photographs were taken on each side
of the intersection to recreate Google Street View. These photos were then
stitched together using Hugin. Panoramas like the one featured below were
created. This is a view of Spine Road at the intersection with Lansdowne Road
Extension. Note the shebeens located to the left (by the red umbrellas).
(Photographs courtesy of Prestige Makanga)
Ana Brandusescu
Nadine Schuurman
Renee Sieber
(Photographs courtesy of Prestige Makanga)
Hugin

2. Using 3D to visualize environmental risk factors
Building a Google SketchUp model
The Google SketchUp (GSU) model was based on an intersection in the
informal township, Khayelitsha: Spine Road and Lansdowne Road Extension.
2. The tracing of the roads and the buildings began. The buildings were
designed based on Prestige Makanga’s photographs and GSV. The 3D Google
Warehouse was used to find objects to construct our built environment
(i.e. cars, people, benches, lamp posts, etc). Textures were used from
Mayang’s Free Textures and Google’s image finder.
 Unpaved ground, puddles formed;
people are forced to walk on the
street; the area that has unpaved
ground, is parallel to the unfinished
sidewalk
 Unfinished sidewalks
 Shebeens – people under the
influence of alcohol can walk out into
the street at any time
 People walking on bicycle paths,
despite of sidewalk in tact
 Faded cross walks
1. We started the model by finding the respective area in Google Earth to
capture the 2D satellite imagery and use it as a face layer and tracing base in
GSU. Both programs had to be open at the same time. However, in the new
version, GSU 8 Google Maps is integrated in GSU, facilitating this step. In the
imagery, the dimensions of the roads, and building roofs are the same as in
real life.
A new model can be built to represent
changes in the area. Geocoded photographs
(displayed alone or as Hugin panoramas) are
critical because they are the main tool to
create the model with. In Google Street View,
once a street has been captured, Google has
no incentive to go back and update it. Thus,
changes that are made in the built
environment are not visible to outside
viewers. However, it is difficult to find
somebody to render a new model.
Google SketchUp 7
Google SketchUp 8
Making connections between street quality and accidents:
Ana Brandusescu, Nadine Schuurman, Renee Sieber
Above is the GSU model we
built, not including vehicles
or people to get a sense of
the environment without
the idea of motion.
Confidence is the main supporting factor for the Google SketchUp
visualization. We must be careful with the false sense of accuracy it creates.
The realism versus abstraction debate in what determines an effective
visualization is evident here. We made an attempt to make the model as
realistic as possible, without misleading the viewer. Everything about the
creative aspects of it, the texture, the amount of detail in the 3D Google
Warehouse components used, helps develop the atmosphere and heightens
the level of interaction. However, even if the user can now explore different
areas (the 3D factor) without having to be constrained to the linear path that
GSV created, the model was still rendered based on 2D panoramas, created
formally or informally (GSV
and Hugin).
Risk factors include:

3. Building the Google Earth Graph model
The Google Earth Graph (GE Graph) was created by Ricardo Sgrillo, a Google
employee from Brazil. The program is free and downloadable with various 2D
and 3D rendering capabilities, allowing the representation of both Excel and
ArcGIS data. The 3D bar graphs were simple to render, which confirmed the
clarity of use and manipulation ability of the program with point data. The
latitude and longitude of the centroids found using itouchmap.com/lat long
were able to be clearly represented, along with their appropriate values
(aggregate trauma injuries for each suburb). Although the user does not have
the option of subdividing the categories, a balloon icon can be created within
Google Earth, where additional information can be stored.
1. Suburb point data from Microsoft Excel is copied in the GE Graph input table.
After entering the required information (title, color scheme, graph size, etc), the
GE Graph will generate the 3D bars in Google Earth.
Using 3D to visualize change in trauma injury patterns
2. On the left, we have the
aggregate trauma injuries of
the Woodstock suburb in a
Microsoft Excel table. That
data is entered in the string of
code created using Google
Charts API. After the code is
completed, just copy and
paste it in the URL box. And
voila! You have your pie chart.
(As long as you have an
internet connection, you’re
set). This is generated as the
image is redirected from the
Google Cloud.
5. To eliminate code, we used Google Spreadsheets. We created bar
graphs/pie charts then replaced the image source (<img src>) in the GE
balloon with the published code from Google Spreadsheets.
3. The Google Earth Outreach website
has templates available for use to
create your own description balloon.
The template includes the code. The
sample placemark and final description
balloon is shown on the left.
4. We have modified the Google Earth Outreach template code. The image
source, titles and subtitles were changed to properly represent the trauma
data. Part of the code of the description balloon is shown, with the final product
(above, right) as it is rendered in Google Earth.
Ana Brandusescu, Nadine Schuurman, Renee Sieber