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:

IntroductionSII-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

Using 3D to visualize environmental risk factors

Building a Google SketchUp modelThe 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 7Google 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:

Building the Google Earth Graph modelThe 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