Image Processing Reference
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by the place that has been created, leads to the hypothesis that some variability in
human behavior may be captured in surrogate form by knowledge of the variability
of the built environment, along with data from the census that provide surrogate
measures of the social environment. In this conceptualization, the built and social
environments are intimately entwined, but not completely dependent upon one
another. The same built environment can host variation in the social environment,
and the same social environment can exist within a range of built environments, but
I would suggest that a relatively narrow range of combined values of the built and
social environments will describe a unique set of urban populations.
Remotely-Sensed Data as Proxies for the Built
Census and survey data provide most of the knowledge that we have of the social
environment of places. Yet, one of the difficulties of using only census or survey
data is that people are enumerated or surveyed at their place of residence. Since
urban residents typically work in a different location than where they live, this
spatial mismatch has the potential to produce a bias in the classification of the
urbanness of place. An example might be a central business district which has only
a small residential population, characterized largely by lower-income persons in
single-room occupancy hotels. Census data might yield an index that indicates a
relatively low degree of urbanness, based on a fairly small population and/or low
density. Yet, the daytime population might represent a large number of commuting
workers, and if they were to be counted the place would score much higher on an
urban index. However, to accommodate that daytime population there must be a
substantial built environment that includes a range of structures, infrastructures and
other features indicative of urban lifestyle.
The built environment could be described by databases that document the type
of structures and infrastructures comprising each parcel of land in every place. The
cost of generating and maintaining such a database is
enormous, however, and we do not really expect that any
but the wealthiest of cities will be in a position to do that.
In the meantime, it turns out that remotely-sensed data
offer a way of generating reasonable proxy variables of the
built environment, and thus of an important part of the way
that places differ from one another with respect to urban-
ness. The modification of the physical environment that is
characteristic of urban places can be inferred from the
classification of multispectral and panchromatic satellite
images. A place that is distinctly urban can be determined from the imagery regard-
less of the characteristics of the residents and we then have an indirect way to
capture the characteristics both at the place of residence and at the presumptive
place of work.
data offer
indirect ways to
measure the
urbanness of a
place regardless
of who resides in
that place
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