Spatial video geonarratives and health: case studies in post-disaster recovery, crime, mosquito control and tuberculosis in the homeless

SVG is an advance on other geonarrative approaches as it adds a visual component,
the spatial video, which can be used as an additional contextual resource when investigating
(and mapping) the commentary. Video are collected, usually using an automobile, with
a latitude and longitude coordinate stream being simultaneously attached to the video
stream. The post-processed spatial video can be thought of as similar to Google Street
View, a spatio-visual reference which is gaining in popularity as a source for digitizing
information into a GIS 21]–23]. Unlike Google Street View, SVG data collection is in the control of the researcher.
The spatial video “kit” utilizes an off-the-shelf extreme sports camera (Contour +2)
with an inbuilt GPS. The camera has associated free software (Storyteller) for displaying
the video and video path. Having access to this free software is important as the
GSV method should be a collaborative tool not only allowing for local expertise to
be mined, but also giving researchers, activists, or concerned individuals a “voice”
in how their local health situation is monitored and analyzed. Indeed, of the four
case studies presented here, three involve local collaborator collected data. Past
spatial video projects have been well documented with topics ranging from post-disaster
damage and recovery 24], 25], mapping the built environment in association with the crime-health nexus 26], and most recently, to identify street-scale health risks in Haiti 27]. In addition, the team has utilized this technology in multiple other countries,
including Bangladesh, Belize, Cambodia, China, Kenya, Liberia, Malawi, Tanzania and
Zambia. For this paper we will focus SVG examples based in the United States. Although
spatial video has been collected using a variety of modes (car, motorbike, bicycle,
boat and by foot), the most frequent approach is mounting four cameras to the windows
of a vehicle. The following seven steps provide the basic procedure for most SVG.

Spatial video

Two to Four Contour +2 cameras are positioned around a vehicle using suction window
clamps.
^a
For maximum clarity these cameras point out above a rolled-down window. In environments
perceived to be more sensitive or dangerous, the cameras can be mounted on the inside
of the window for unobtrusive data collection. High definition video means that the
car can drive at road speed as a later paused image is clear enough to provide a suitable
digitizing source. Multiple cameras mean that there is compensation in case one video
recording on either side fails, and if three of the four GPS fails. Digitizing information
from the video into Google Earth is appealing as both Contour Storyteller (the software
available with the camera) and Google Earth share the same imagery 27]. The camera needs no outside GPS antennae, and is turned on by sliding the rocker
bar forward. A series of lights flash to give a status update on the battery, micro
SD card, and GPS fix. The battery typically lasts for 2 h, but can be extended to
six if connected to a car charger. On full HD mode the 32 GB card can record for approximately
4 h. Some pre-GPS work is required to make the camera fix onto satellites quickly.
For example, making a pre-fieldwork connection to Contour Storyteller allows for the
software to be updated with satellite positions, and turning the camera on some time
before the ride helps with locking in.

Geonarrative

Commentary is recorded directly by the cameras, or (preferably) onto a digital recorder
with an output from that device being split and fed into each camera. The camera contains
an internal and external microphone. These can suffice if the cameras are close to
the subject, and if video is being recorded through the window. If the vehicle is
large, loud or has a separation between the front and back seats (such as a police
car) then an external microphone is used. The Contour +2 does not provide power through
its microphone input connection, so a powered external microphone is required. The
best setup is having the subject hold a microphone which has an output cord going
through a splitter and then input into each camera. It is vital that a sound can be
matched on the external microphone and the video in order to interpolate words over
the GPS path.

Data collection

The typical ride lasts between 45 min and 3 h. The “team” includes a driver, a camera
operator (usually in the back seat), and the subject sitting as a front passenger.
^b
During the ride, the driver and the camera operator help facilitate the interview
by asking prompting and clarifying questions. The ride starts with the reading of
an IRB-approved permission sheet. Depending on the project, there is a collection
of basic socioeconomic and subject histories (especially how long he/she has lived
in the area being collected), and a perceptual map exercise. This is useful to start
each ride in the same way, to get each subject in the same mindset, and as a future
research project of comparing perceptual maps to SVG content.

Data download

On the culmination of the ride, data are downloaded from each camera either by plugging
the device into a computer or extracting the micro SD card. Once downloaded, two post-processing
stages occur: renaming all video files and the creation of a metadata sheet. The size
of video output files from the Contour +2, especially in Full HD 1040p mode, is approximately
1 GB per 9 min. The camera software breaks the video stream into approximately 36 min
segments. After the ride, each of these segments is renamed according to the date,
the side of the camera, and the sequence of the video file within the total number
of segments.
^c
The GPS from each video segment is extracted using Contour Storyteller (as GPX) and
each file is imported into Google Earth. By saving all video sections for one camera
as a “save place as”, a KMZ is generated of the entire route. This can be imported
into ArcGIS 10.2 so that each path (the number of paths equals the number of cameras)
can be compared for performance.
^d
A metadata sheet is created in word processing software and contains an image of the
route map exported from the GIS, a brief summary of the ride (notes on the subject,
about the commentary, and any technical problems), the name of each camera and its
position on the car, and an assessment of the video, audio and GPS quality for each
camera. These sheets are an imperative companion to the archived video, allowing for
easy reference in future projects. Metadata sheets are also important for SVG analysis
as they facilitate combining the best audio and GPS source.

Transcription

The SVG audio is transcribed using any software of choice. Although this can be done
in Microsoft Word, for example, specialist software allow for easy time stamp insertion
and easy control over moving the audio back and forth. The final output must include
six numbers (hours, minutes, seconds) separated by colons. Time stamps precede any
substantive comment, usually every sentence, and may even be inserted before key spatial
locations, as long as each starts a new text row. As the specially designed software
interpolates words between time stamps, having a stamp close to an important statement,
and followed by a second stamp will help with mapping words to the precise location.
Time stamps must be sequential, and duplicates are not allowed
^e
.

Spatial word interpolation

The time stamp of a key sound on the audio recording is matched to the audio on the
video source allowing for the “real” time stamp of the audio to be established. Again
using the export GPS function in Contour Storyteller, but this time using a CSV format,
coordinates and their associated Greenwich Mean Time (GMT) can be displayed in Excel.
This also gives the first time where a GPS fix occurred, which may be different from
the beginning of the video media time. It is important to match sounds on the recorder
and the video after this fix has occurred. The High-Performance Computing and GIS
Lab (HPCGIS) at Kent State University has developed computer code (from this point
G-Code) that interpolates the transcription across the route and calculates the “real”
time stamp of each spoken word. G-Code requires the transcribed commentary to be uploaded
in .txt, and the associated GMT time input for the beginning of the file. All words
occurring between time stamps are interpolated across the number of seconds between
time stamps. If there is a problem in the transcription, for example two identical
time stamps, the code stops processing and identifies the time where the problem occurs.
After the .txt file has been edited to remove all errors, and G-Code has successfully
run, the output of three columns (ID, time, word) is copy and pasted back into Excel.
Excel manipulation is required of both the CSV and G-Code output to ensure that the
GMT time stamp column in both is ready to be joined together in a GIS. Manipulation
includes saving both files as .prn files, then reimporting them so that the columns
of text are appropriately separated.
^f

Mapping words

Both the spatial video file (CSV manipulated into an Excel format) and the G-Code
output (saved as an Excel file) are added to the GIS. The spatial video file is added
next, with latitude and longitude plotted to produce the data collection path. The
G-Code excel file is added and then the new route shapefile is merged with this using
a table join (order is important as each word needs a coordinate). This merged file
is plotted using the latitude and longitude columns, and the word column is displayed
as a label for each point. At this point the SVG can be mapped, manipulated, queried,
and analyzed like any other GIS point file.

There now follows four case studies showing the utility of this approach with minor
method manipulations detailed depending on the subtleties of the project.

1. Mapping perishable data: the recovery and psychopathology nexus after the 2011
Tornado in Joplin, Missouri

“I didn’t go there very often, I’ve been there since they’ve rebuilt but I’m always
gonna remember there because that unfortunately is where I saw the first people – the first time I saw people were killed in the tornado in a car up there.” – SVG Participant

Researchers have previously considered the geographic perspective of the health-hazard
nexus 28], though the role of micro spaces is still largely undeveloped. Spatial video has
previously been used to match mortalities to damage patterns, and then to monitor
spatial patterns in recovery linking these to potential social outcome data including
stress and crime 26], 29]. If we focus primarily on the recovery phase of the disaster cycle, then manifestations
of poor health in the literature largely concern psychopathology 30]. Of these, what few spatial studies exist tend to miss patterns at the micro spaces
scale primarily due to data availability 31]. SVG is a method that can mine the connection between how events during and after
the disaster affect and continue to affect day-to-day activities, in a spatially specific manner. To illustrate the potential of this new research direction, a series of SVG were
conducted in Joplin, Missouri primarily during 2014 using an unstructured interview
format where the subject described any tornado related event with only minimal questioning
and clarification. The resulting narratives were transcribed, and imported into G-Code
before mapping in ArcGIS 10.2. Just as with the opening quote to this case study,
many of the comments are directly related to the night of the tornado. However, the
conversation often hinted at the psychological burden still being carried. Although
it is possible to overlay multiple SVG to create a new data layer of the common experience,
for an extreme event such as this, there is also value in the single experience as
an indicator of the types of challenges and stresses felt while moving through recovery.
In this regard, although specific locations can be mentioned (the corner where the
bodies were seen) it is more useful to think of the insights as spatially “fuzzy”.

Comments can be used to gauge the spatial heterogeneity of recovery, or as in the
following example, what it was and is like to live in the aftermath of a disaster.

“Well, for a while it was just deserted. And our home was – we have a new home there. Within a little over a year after the tornado. But it was
one of the only structures around. It was dark and it was deserted. I’d say within
another year there was a bunch of more substantial rebuilding, and still going on.
But I’d say a disproportionately high number of homes there are Habitat for Humanity
homes inhabited by people who did not live in this area before the tornado.”

If we reconsider the previous literature on psychopathology, the SVG not only supports
existing theory (evidence of triggers, negative or positive coping mechanisms) but
also illustrates how the situation of the individual, both during the disaster and
recovery phase, is related to health outcomes and the need for spatially targeted
intervention. It would be interesting to see how disaster psychologists could further
mine these data, or this data collection approach, in their research.

“In the course of a normal day, I’ll always find something that reminds me of it.”

Method variant

Six different SVGs were uploaded into the GIS. One manipulation included using a key
word query to identify the locations of important phrases, such as “recovery”. By
using the time stamp associated with each word coordinate, it is possible to return
to the original transcription to gain further context of what was being described.
Another easy task is to go to an area of importance, such as a particular street segment,
to see what was described at that location. In Fig. 1 six different SVG intersect (each a different colored point) at a key location on
the tornado path where multiple mortalities occurred. Although it is hard to read
this graphic as whole, it should be thought of as a SVG index. By zooming into a location
such as this, each GPS point, or displayed word, can be selected and matched back
to the original transcription. Here the full commentary can be read and questions
asked such as how do different people comment on this location, how many refer back
to the events of the day, and how many comments focus more on recovery or the current
setting. For any of these queries or manipulations, the associated video for that
ride can be accessed to see exactly what was being described.
^g

Fig. 1. Six different SVG intersect (each a different colored point) at a key location on the tornado path where multiple mortalities occurred.

2. Mapping contested spaces: multiple perspectives for the same geographic area

“It’s reduced crime, but it’s because it’s reduced the life, it’s thinned it out.
And urban renewal is urban removal.”– SVG Participant

Another compelling reason for conducting multiple SVGs is to assess how perspectives
on a neighborhood may vary. This has implications with regards to understanding a
health problem, and developing subsequent interventions. For example, it is widely
acknowledged that there remains a disparity in birth outcomes for African Americans
especially in large urban areas—a situation which has not really changed in the last
two decades. Mapping these disparities often only verifies what any local health worker
already knows 32]. The challenge is to “contextualize” data, since a spatial hotspot analysis only
tells a partial story—it identifies a (statistically significant) concentration of
events without providing explanation or causation 33]. Research might validate some of the patterns identified, but limitations still exist
with the type of data available (for example linked infant birth and death certificate
data). Many practitioners would argue that there is invaluable insight to be gained
from those who live and serve the hotspot areas, if only it can be accessed and analyzed.

Although a limited number of SVGs, as in the Joplin example, can be illuminating,
this constrains the analytical possibilities. Having multiple perspectives provides
a more holistic impression, and through triangulation 34], offers validation with regards to the places, spaces or ideas that are identified.
The opening quote to this case study displays one perspective on the dichotomous responses
regarding a classic “contested space” in Akron, Ohio. A city initiative in the mid-2000s
removed a large number of blighted homes from the neighborhood. From a police perspective
the move was a success. For some residents, the neighborhood itself has been winnowed,
both physically and socially,
^h
with overall crime being reduced as a result of the associated reduction in population,
though more specific and localized hotspots remain. This changing urban setting generates
several other problems for local area health professionals, such as the high rate
of domestic disturbance call-outs, many of which occur with children being present
(this involvement of children can be extracted from Akron Police Department data).
As such, this neighborhood is an example of the health-crime nexus found in many inner
city areas; high levels of violence (and child exposure to violence), high injury
rates, high chronic disease rates, substance abuse and as suggested by the domestic
call outs, a high level of family stress 35]. Although intervention is needed, what form should it take? Indeed, although we have
outcome data, what are the actual perceived problems or pathways? SVG can capture
professional insight (such as police officers), local experiences or emotions (residents
and clergy), and by overlaying and comparing these perspectives, help formulate a
geographic and socially relevant intervention.

The approaches as presented in the first case study can also occur with the SVGs collected
here. For example, one particular Akron Metropolitan Housing Authority (AMHA) complex
in this neighborhood displays as a hotspot for crime, family disturbance, and different
health problems. Although the spatial analysis of call-outs or health outcomes would
identify this as a geographic area of concern, the narrative itself sheds light on
the complexity involved:

“They just have drama here…Some folks will be here for a while and then get kicked
out, so there’s a zero drug policy and zero visitor policy. So like there will be
a single mother but the baby daddy stays and they’ll find out the baby daddy stays
and they’ll kick him out.”

Method variant

Over 30 SVGs have been collected for the subject neighborhood, with the G-Code mapped
paths being overlaid to form a composite impression of the neighborhood. With a larger
number of SVG, different approaches are required to search through text for key themes
or “nodes” in addition to commonly accepted terms such as “drugs”. This can be done
using more sophisticated software, such as NVivo, or even a simple word cloud.
ⁱ
For example Fig. 2 displays word clouds constructed from the SVG for two main groups: residents and
police. What is immediately obvious is the different way the area is described, which
is understandable given the overseer role of the police while residents (generally)
focus on the positive aspects of their home space. Just as with the Joplin example,
any word from these clouds can be queried and then mapped, either for a single SVG,
or in total for all subjects. The combined SVG maps can be spatially analyzed using
a variety of different point pattern techniques (the key word is the numerator, all
words are the denominator) to find statistically significant concentrations of a phrase.

Fig. 2. Word clouds constructed from the SVG for police and community members.

Consider Fig. 3 which uses kernel density estimation (KDE) to display all mentions of SVG “meth”
by the police, and then by community members. All drug “call outs” by the police are
also displayed as a comparison with an official incident data layer.
^j
The example KDE intensities are overlaid on properties which have been visually assessed
and mapped using the spatial video as a source.
^k
Even from these three comparative layers we can see that community knowledge differs
from both the police and from actual response calls, especially for one street segment
which resonates across the community SVG. The visual quality of buildings can be used
to help explain why these patterns exist. For example, on the street identified as
problematic by community members, little building clearing has occurred. However,
there are also no examples of blight with only two residences being classed as “poor”.
Imagine the additional insight gained by adding more traditional health data, such
infant health outcomes, to these maps.

Fig. 3. Kernel density estimations (KDE) of all mentions of SVG “meth” by community members
(green) and by the police (blue). As a part of the overlay, police calls for service are shown in red and output from a spatial video built environment survey are displayed for each parcel.

3. Mapping “institutional knowledge”

(00:14:00) Prior to closing this up, I could stick my dipper in there, you know a
foot to a foot and a half.

(00:14:10) I used to set my traps up here but I haven’t caught anything when I set
it up here.

(00:14:17) But if you go over here.

(00:14:33) And if I set it up over here, it’s pretty moist and there’s probably water
inside.

(00:14:41) And in this basement area right here I put a trap in here and I catch Aedes
and Culex in this area right here.

The focus of the SVG can be to capture the knowledge of health professionals who “work”
an area by spatially capturing their experiences. The danger of not recording this
knowledge is that when a key employee leaves then most of his/her knowledge disappears.
On a recent SVG with an environmental health specialist focused on lead exposure in
Akron, Ohio, this point was raised. Although the initial response was that the next
specialist would soon achieve the required certification, during the ride it became
evident to the interviewee as she listened to her own comments that she had amassed a vast knowledge of local area risks, solutions, and especially
atypical problem solving. One example being the ability to visually assess a property
to gauge the likely lead threat posed. At the end of her SVG the first temporal layer
of an institutional knowledge map had been established. Now a new worker could re-watch
the video and listen to her narrative.

This approach is also beneficial if more than one professional covers the same area
as each learns the same on-the-ground information separately often with little ability
to share knowledge. Capturing health-related institutional knowledge is not limited
to social health situations. As an example, consider the following SVG for mosquito
control for a mid-sized urban area.
^l
The vector control team had been diligently controlling mosquitos for many years.
Different environments pose different mosquito “problems”, and for this settlement,
the biggest challenge was seasonal rains and the type of drainage system in situ.
Other important considerations were the efficacy of different mosquito control strategies,
the proximity to vulnerable populations (especially daycare centers), and where positive
disease cases (of West Nile virus) had been found. One of the reasons for capturing
the SVG, beyond establishing a record of control approaches, was an impending concern
of Dengue and Chikungunya, both diseases having been found within 100 miles.

Method variant

Just as with the first two case studies, G-Code was used to map out every word from
a day-long drive around the study area that covered all past and present mosquito
trap locations. Different key words important to mosquito control such as a specific
mosquito species were mapped. However, as the purpose here was to create a map of
institutional knowledge, the narrative was re-read for all commentaries that had significance
to mosquito control, and could be directly tied to a location, such as a building
or field. These segments of text were then rejoined back to the mapped text in the
GIS (again using the GMT stamp) so that the coordinate of the first word became the
anchor for that comment. Further spatial manipulation occurred if the point was in
reference to a specific location such as a building by editing the SVG coordinate
onto the shape as identified by comparing the spatial video with high resolution aerial
photography in the GIS.

Figure 4 displays the abandoned building references in the opening quote of this case study.
The figure also displays the software used to view the video, which normally shows
the associated map in the inset box, though it is covered here with the second still
to preserve locational anonymity. Two examples are chosen; the main video still is
from the vehicle as the team stops at this building known for mosquito problems. The
inset still is when one camera has been removed and the team walks the premises to
talk about the exact location of the problem areas and traps, in this case the flooded
section of the basement is visible. Unlike with the first two case studies the Contour
+2 camera was removed from the window mount and hand carried along with the microphone.
As long as neither the camera nor audio recorder is turned off, the same SVG method
applies.

Fig. 4. Snapshot images from both a vehicle and then hand held SVG to capture institutional
knowledge.

As the main purpose of collecting an institutional knowledge SVG is as a resource
or archive for the professional organization, it is important to have a ubiquitous
means of dissemination. To do this, point commentaries were exported to Google Earth.
This resulted in a series of pins that would reveal the relevant commentary for that
location in a format that could easily be emailed to any associated worker. A section
of the reference map before Google Earth translation is seen in Fig. 5. All spatial information has been removed at the wishes of the mosquito control board.
Apart from the example statements that are tied to a specific location, key words
have been extracted from the G-Code, allowing for the easy mapping of any trap location,
or for a specific mosquito species. Just as with Case Study 1, these data are more
limited for analysis. However from a practical perspective, reviewing these Google
Earth maps at the beginning of each season could help with strategic planning. Repeating
the exercise at each season’s end would also grow the institutional archive. The archive
is further enriched by the spatial video, with each comment or insight being accompanied
by the actual image, as seen in Fig. 4.

Fig. 5. A section of the SVG institutional knowledge reference map before translation into
Google Earth.

4. Mapping the homeless in Los Angeles

(00:07:34) So, you’ve got different types of homeless, which I know it sounds really
crazy, but the guys that are on this side of the freeway,

(00:07:40) are definitely a little bit, um, rougher around the edges, than the people,

(00:07:47) on the other side of the 101. And the reason why is because you’ve got
MacArthur Park,

(00:07:51) you’ve got like, more of the suburbs, you’ve got more areas where, um the
hills.

(00:07:57) Um here, it’s concrete jungle, and they’re completely trapped.

(00:08:03) So you’ll see them, their like, their sixth senses grow a little bit harder.
Their vibe is a little bit more intimidating.

(00:08:09) It’s just because they’re in the middle of the jungle. That’s my impression.

(00:08:17) When I first started coming down here, you would never see families with
kids.

(00:08:22) When you go to the jewelry mart, you’d never see families with kids. You
would see crack deals.

One of the biggest geospatial health challenges in the United States is how to capture
health compromised cohorts with no typical address 36], 37]. Two examples are the “homeless”
^m
and sex workers. The former by definition have no address, while for many sex workers
there is no constant abode, often moving between temporary locations. Both groups
have high health risk factors and we would expect them to show as hotspots if these
cohorts are captured in normal surveillance data. As reliable spatial data are not
available, we are left with three challenges: it is hard to know where spatially high
concentrations of a disease occur, and who are at risk in primary proximate locations.
Secondly, we have no idea as to the activity patterns and daily mobility paths that
might help explain or predict disease spread. Linked to this issue, we have only a
limited understanding of how and where best to intervene. Tuberculosis and sexually
transmitted diseases are perpetual health concerns associated with the homeless and
sex workers in Los Angeles, and yet there is a dearth of understanding about these
cohorts, especially from a spatial perspective (Fig. 6).

Fig. 6. A literal pin map showing the application of Narcan medication located in a non-profit medical facility
in skid row. The map which was photographed on the wall of the facility shows the scale of the problem,
but none of the other attribute information about each victim is recorded.

Mapping proxies for the homeless have been tried, such as discarded drug needles 38]. Just as with the three previous case studies, SVG provides a novel alternative to
help explain current homeless spatial patterns, and as an insight into what it is
like to be homeless. Spatial video can be used to help map visible physical locations,
while SVG can capture individual experience, contested spaces, and institutional knowledge
from those who serve these areas. Two ongoing spatial video and SVG projects in Los
Angeles have focused on knowledge of health outreach professionals south of downtown
regarding “spatially challenged cohorts”: the homeless in skid row and sex workers.
Consider the following SVG conducted in Skid Row. A longer section of narrative is
provided to help illustrate the spatial richness of many SVGs.

(00:20:16)[DR] How come there is no one on the other side?

(00:20:18)[HW] Sun

(00:20:19)[DR] Oh the sun

(00:20:21)[HW] So they basically fort, I mean it’s a lot of work to be homeless. You
have to find the right spot,

(00:20:26)[HW] you have to make sure there’s no sun, you have to make sure that people
aren’t going to kick you out,

(00:20:30)[HW] you have to make sure you’re not coming on someone else’s block. You
have to make sure like.

(00:20:36)[HW] It’s a lot of work.

(00:20:39)[DR] And are they, are, you said people are loners, are some people actually
group, gregarious and connected to each other or do they look out for each other?

(00:20:50)[HW] yea there’s a sense of community.

(00:20:53)[HW] There’s a sense of community for sure. But you also have to keep in
mind, what drugs are they using?

(00:21:00)[HW] because and what time of the month, what time of the month is it. Are
they going to be sharing their drugs in order to have friends,

(00:21:09)[HW] Are they going to be just doing all the dope themselves, …. the time
of the month, matters a lot.

(00:21:17)[HW] See there are too many gates here

(00:21:20)[DR] What do you mean too many gates?

(00:21:21)[HW] Too many gates, it’s not a safe place to hang out.

(00:21:26)[DR] Because too much traffic?

(00:21:29)[HW] Too much traffic, too much activity.

(00:21:33)[HW] She’s probably looking for a rock.

(00:21:45)[HW] So I want to go, so you see how that’s safe there?

(00:21:52)[HW] That’s a safe spot.

(00:21:53)[HW] uh huh, that’s a safe spot.

(00:21:57)[HW] So as I said, when we first started, we were on Fifth and Main and
we came here in two thousand five.

(00:22:41)[HW] And this is kind of where it starts.

(00:22:46)[DR] Do all those people camp together? Do they know each other?

(00:22:49)[HW] Mhmm

(00:22:58)[HW] See they respect Skid Row Housing Trust, they’re not on that corner.

(00:23:04)[HW] But now they’re over here.

(00:23:33)[HW] I mean that alleyway there’s a lot of stuff that goes on. A lot of
stuff that goes on because you’ve got these guys on the corner, they’re slinging,
they’re hustling.

(00:23:46)[HW] They know what’s up. You’ve got the watchdog, you’ve got the old man,
you’ve got the one who’s carrying it and you can tell by their shoes.

(00:23:54)[DR] Who the dealers are, who’s dealing?

(00:23:55)[HW] Yea. You can tell by how clean their shoes are.

(00:24:03)[HW] How white their shirts are

(00:24:08)[HW] Do you see what I mean, do you get the vibe?

(00:24:13)[DR] So is this where the alleys are to the left?

(00:24:17)[HW] The alley is right here to the left.

(00:24:19)[HW] And this is always where they comingle.

SVG captures location specific and more general information with regards the homeless.
Insight is gained into the micro spaces of where the homeless set up camps, as well
as specific places of risk and safety. There is also more general temporal information
with regards to the social cohesion—drug nexus. Personal experience also reveals the
micro space indicators of drug activity.

Method variant

The spatial video is used as the primary data source with visual aspects for Skid
Row being digitized either into Google Earth and then imported into the GIS, or digitized
directly into the GIS. Consider Fig. 7 which displays the locations of homeless camps, red shopping carts which had been
bought for the homeless,
ⁿ
and people with visible health problems such as a wheel chair. Each location represents
an object digitized from the spatial video and then visualized using contours from
a 50 m KDE. Questions that arise from these maps, such as why is there spatial variation
in the location of camps, can be investigated using the approaches described in the
first case study. Insight into the homeless lifestyle can be gained either by using
the map and selecting the word path on the map for the area of interest, or by going
to the SVG and finding relevant sections of text, in this case that reveal the camp
location decision making process.

Fig. 7. Locations of homeless camps, red shopping carts, and people with visible health problems such as a wheel chair.

It is easy to imagine how these types of insights can help inform intervention strategies.
By repeating spatial video runs over multiple time periods, homeless camp stability
can be assessed and even proxy addresses given to the residents. On top of these layers,
SVG provides invaluable insight into the daily activity patterns of the homeless.
At the time of writing, another TB outbreak has occurred in this general vicinity.
Imagine if SVG is used as part of a surveillance strategy whereby interviewing those
infected, becomes part of a standard approach to identifying activity spaces, mobility
patterns and camp locations.