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Published on 22 Dec 2015
Heroin scoring and injection in Melbourne AS (Dovey et al). The authors undertake a socio-
spatial analysis of injecting drug-use in public space. It focuses on one urban district in
Melbourne, Australia, which has become identified with heroin sale and use in public space.
Selling activities are camouflaged within a diverse street life while injecting sites are dispersed
through laneways, car parks and toilets. These injecting zones occupy liminal places that slide
between categories of private and public, and that mediate complex and paradoxical relations
between safety and danger. Those who inject in public space are caught in a dilemma of needing
both privacy and exposure in the event of an overdose; safety from police becomes danger from
an overdose. This empirical work, based on interview and spatial analysis, is presented as a basis
for theorizing the socio-spatial construction of heroin use and for assessing the prospects for safe
WEEK ONE: Dovey et al, 2001, melbourne smith street study
Case study is heroin scoring and shooting up on and around Smith street. Study area is a
down market strip of smith street. Has commercial and some industrial lots. Has back
alleys, parking lots etc .
Research Question: After purchase, where to heroin users go to shoot up? While some
users take the stuff home, there are street users; study focuses on street users and their
behaviour. Want to study drug use obtrusively, not allowing the users to know their, being
studied. To do this Dovey uses discarded heroin syringes as evidence.
Process Being Sampled: a process is a recurring behaviour that can be characterized by a
statistical population. A case is a person often being studied. Here the process is the
choice of location for heroin scorers to shoot up. Author simplifies reality, inner battle
between going far away to be out of sight, quick enough to use the drugs without them
being found on them, but public enough so people can find the subjects in case of
Two questions arise, first is that are drug users rational\/ based on the addictive nature of
drug users, they do not seem to be displaying rational behaviour. Secondly, there isn't
much about street use shooting up that is specific to an individual.
Popular places for heroin shooting up included laneways, alcoves, public toilets and
parking lots.
Whose behaviour is being modelled? Here it is the heroin user' behaviour that is being
modelled, however other people (such as general public and police) affect the behaviour
of drug users.
Assumptions about behaviour: Author makes 4 assumptions in their study. Author
assumes heroin users are purposeful, assumes street life provides camouflage, assumes
drug user unsure about quality and overdose, assumes shooting where drug paraphernalia
is found.
Type of Study: Exploratory, there is no X variable being tested.
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Nature of Data, Observation: Because the author wants to be obtrusive in their study, they
cannot view a person as a trial or an experiment. Instead, Dovey treats unofficial
personally outlined areas to find paraphernalia. In conventional application of scientific
method, 4 steps (model identification including theory, data included from lab
experiments including given and controlled situations, lab experiment and data, and
model validation including error analysis and hypothesis validation. All of this leads to
scientific method, something the authors did not do.
Often use survey or archival data, two steps to this; (1) exploration including literature
review, data assembly, model exploration and induction. (2) model validation including
statistical statistical methodology and hypothesis testing. Each observation then has a set
of conditions (X) that causes the outcome (Y).
Creation of the map untold, although assumes used through low altitude aerial photos and
existing governmental blueprints (archival). Used AM/FM or CAD techniques. Had to
use orthorectification in order to correct for distortion, altitude variation and curvature of
the earth.
Authors do not say what a zone is.
Statistical Population: Authors not sure what statistical population this study applies to.
Model: No model is used in this study.
Unobtrusively: they don’t want the behavior of users to be affected by their research project
Homogeneity: Homogeneity is a loose term to mean that the same model applies to every case in
the statistical population. However, this does not mean that every case has the same outcome. In
modeling, we imagine a deterministic part and a stochastic part. If two cases have the same level
for each independent variable, their deterministic parts will be the same. However, the notion of
a random variable, something that causes the level of Y for two observations to differ even if all
independent variable values are the same, explains why Y differs between them. Following the
treatment of a random variable, we typically imagine the stochastic component as having an
expected value of zero and a constant variance. The property that two observations can have
different realizations and yet have a stochastic component with a fixed variance is termed
Risk: Probability, likelihood, or frequency with which an outcome occurs. Alternatively, the
proportion of cases that exhibit a common feature.
Exploratory date analysis - A field of study long associated with the work of John W Tukey, this
is the methodology of exploring data to discover relationships without imposing particular
mathematical structures and mindful of the presence of outliers. Topics range from visual
techniques (e.g., stem-and-leaf, box plots) to re-expression to plots of relationships and
straightening of plots to smoothing and polishing to robust estimation.
Feature: A generalization of the observed spatial pattern of entities. Typically, this includes a
cluster (hot spot), a hole, or a ridge on a map layer.
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Y – layer entity - In GIS, the Y-layer maps the set of entities whose placement (SQ) or rate of
activity (RQ) we seek to explain.
Model identification: Procedures and methods by which the analyst selects the form of model
and the specifications of independent and dependent variables to be used in model estimation.
Model identification includes literature review, data assembly, model exploration, and induction.
Model validation: Procedures and methods by which the analyst assesses the appropriateness of
the selected model.
AM/FM - Automated mapping / facilities management. CAD applied at the level of buildings,
machinery, and fittings. A cousin to GIS software.
Null and alternative hypotheses: In science, falsification proceeds from the consideration of two
contradictory hypotheses about the population (process) under study: a null hypothesis, usually
exact (e.g., μ = 0), and an alternative hypothesis, usually inexact (e.g., μ&νβσπ; ≠ 0). The null
hypothesis can never be proven. A set of data can only reject a null hypothesis or fail to reject it.
Because of the nature of "empirical proof", we set up the null hypothesis as that we we aspire to
reject. The term "null hypothesis was first used in 1936 by the statistician Ronald Fisher. Jerzy
Neyman and Egon Pearson formalized the notion of the alternative hypothesis.
Zone (feature): A generalization of the observed spatial pattern of entities. Typically, this
includes a cluster (hot spot), a hole, or a ridge on a map layer.
Spatial context (geography of difference): GIS is useful only when there is a spatial context to
the analysis. In Economics and some other social sciences, the way that we think about problems
is often not spatial at all. However, for GIS to be useful, the phenomenon must be perceived in a
way that highlights the role of proximity and/or connectivity.
Spatial context takes two distinct forms. One form consists of variables that are independent of
the process under study. In studies of processes that are social, for example, we usually treat
physical variables as independent. Here, we would simply enter spatial context as one or more
independent variables into our model. The second form of spatial context is that variables may
not be independent of the process under study. Here, we have to be concerned about selection
Rate question: In a rate question (RQ), the analyst asks why the rate or level of some Y variable
varies from one place to the next. Why does a retail establishment at one site have higher sales
than an otherwise-similar store at another location? Why is the selling price of a house higher in
one neighbourhood compared to another. In a rate question, the dependent variable is typically,
but not always, ratio or interval-scaled. Another variant of a rate question is when we ask about
changes over time: e.g., survivorship. Of the retail establishments that were in the study area a
year ago, which ones are still here today? In this case, the dependent variable is categorical (e.g.,
1 if the store survives, 0 if not), not ratio or interval-scaled.
In the process under study, why does the likelihood, rate, intensity, or level of an outcome differ
from one observed location or area to the next? Rate question. emphasizes the importance of
spatial context. See also site question.
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