Lab 3: Geographic Patterns and Processes
Table of
Contents
Lesson goals
Patterns
What
are indicators?
Patterns
indicate complex relationships
Data
modeling
Example
Public
sewer expansion
Processes
Processes
show changes in geographic relationships
Processes
connect patterns
Accounting
for natural and human dynamics
Example
Reducing
flooding risk
Exercise
Developing
a template for urban indicator
analysis
Lesson summary
1.
Goals
In this lab you will
learn how:
·
to recognize patterns and processes
·
patterns indicate relationships
·
processes show change
·
processes connect patterns
·
develop an analysis template for urban indicators based on Senegal census data
Patterns
and processes are important to spatial decision making
2. Patterns
A map of cyber cafes in
a city shows you a pattern of their locations. This pattern includes major
roads and may also show nearby parks, shops, and eateries. That information
will be helpful if you want to do want to get an overview of a
neighborhood. But, what if you just want to see if your favorite coffee
house has a location near the mosque?
With a GIS you can
explore patterns for a variety of data that a single, static map leaves
out. A GIS makes it possible to construct many different patterns from
geographic information. In this topic, you'll learn how to recognize
patterns, understand what patterns represent, and interpret relationships.
a)
What are
indicators?
Indicators are
representations of objects or the characteristics of objects as symbols
that make many types of map analysis possible. You can analyze any
information in a GIS by creating symbols that indicate environmental or
human characteristics. The example below shows a map of the Ouakam
neighborhoods where the shading shows each neighborhood's proportion of
population at working age (2-39 years).
Darker
colored neighborhoods with a larger proportion of working-age population
The map below shows Dakar with individual streets, highways,
and the international airport. These symbols indicate patterns. Clear
symbols make it easy to read the map. Starting out in Yoff you can trace
the highway to find a route to downtown Dakar. You read the map as a pattern
of indicators that represent real-world objects.
One
route from Yoff to downtown Dakar.
To find out how to
get from Yoff to Dakar, you must first identify Yoff,
and Dakar, and then the highways in between.
b)
Patterns
indicate complex relationships
Patterns are crucial
to showing complex relationships. In the previous concept you used a map of
highways to determine a path from Yoff to downtown Dakar. You can also determine what
lies along the route as you drive from Yoff to Dakar. An alternative route will take
you along the coast, avoiding most of the heavily settled areas. Knowing
what landmarks you'll pass through will allow you to evaluate situations.
For example, if you decide to take the coastal route, you may want to stop
by Cheikh Ante Diop University and you can change your route
based on the interpretation of patterns.
Alternative
route along the coast.
You can also
represent more complex relationships through indicators. The graphic below
shows the percentage of people with a college degree in Ouakam. High rates
may indicate areas near a tertiary institution or an employer who hires
highly qualified personnel.
The
darker the shade, the greater number of persons with a college degree.
Of course, there may
be other reasons for a particular neighborhood to have had a large number
of academics that year. Once we include the fact that there is an air force
battalion right to the north, we may conclude that the northern dark spot
houses officers. Also, if this map is based on absolute rather than
relative numbers, then the dark spot in the center might only represent one
or two medical doctors affiliated with the hospital in this otherwise
sparsely populated census unit.
You can explore the
hypothesis that the higher percentage is an artifact of a low overall
population number by examining the pattern for Texas counties. Looking at the map
below, you can see that this is indeed the case.
Numbers representing the number of
persons living in each census unit.
c)
Data
modeling
Patterns are
abstractions of things or of characteristics. They represent tangible
objects in the environment (like a highway) or qualities (like housing
vacancies). To create a pattern you need to know what the pattern refers to
and what it does and doesn't include.
The population of Dakar only includes the people who
live there; it doesn't include people who work there. Choices about how
indicators are constructed and measured, to a great extent, determine what
can and cannot be done with the geographic information. These choices are
part of data modeling.
Data modeling is how
you structure, measure, and organize these indicators. They are
observations of characteristics of the real world, but are always limited.
For example, the census only collects data about where people reside, even
though they spend much of their day somewhere else. If you are surveying
small children’s’ health in residential areas, then the geographic information
from a census is the right information. If you want to survey childcare
centers close to a mother’s workplace, census information for the area
won't help.
Data modeling
reduces information about the world to make it more manageable. The choices
you make about how to condense this information always lead to advantages
on one hand and disadvantages on another. When making spatial choices it is
important to understand the possibilities and limitations of the data you
are working with. You might not be able to change the data modeling, but
understanding the choices will help you make better decisions.
3. Examples
a)
Public
sewer expansion
Septic tanks can lead
to health problems if the systems are improperly installed or maintained.
For this reason, public health officials encourage homeowners to switch to
using public sewers. Unfortunately making the switch is often very
expensive, so the areas with the greatest need are converted first.
Public health officials
in Yoff have asked you to determine which areas have less than 75% of their
homes or business connected to public sewer systems, but are close to
existing areas with sewers. You will use census tract polygon data to find
the problem areas and prepare a report.
First, the problem
must be restated in terms of the indicators. The attribute percsewer in the
%sewer theme shows the percent of sewer connections in each census tract.
These patterns represent complex relationships between households and
sewage treatment.
Darker
colors indicate a greater number of households with sewer connections.
This first theme
gives you a rough idea of which areas are near well-connected areas, but it
could be improved. For example, the default classification doesn't have a
class beginning at 75%. By reclassifying the data, you can prepare a view
with patterns that clearly indicate areas where more than 75% of the
households are connected to a sewer.
Households with septic
tanks may have any number of economic, political, or geological reasons for
not having a sewer connection. Blasting a trench to lay a sewer line in
bedrock may have been prohibitively expensive. Perhaps, when the data was
collected the sewer line had not yet been completed, but since then it has
been. The many possible reasons reflect the complexity, not only of placing
sewer lines, but of this type of data in general. The patterns that exist
reflect many relationships, but reducing to an indicator does not capture
all the detail.
By deleting the
areas with less than 75% sewer connections and reclassifying of the
remaining data, a new theme called Top 25% is created that shows areas with
between 75 - 85%, 85 - 95%, and 95 - 100% of households with sewer
connections. The census tracts neighboring these areas are areas where less
then 75% of the households have sewer connections and are the prime
candidates for a program to expand existing sewer connections. This theme
provides a pattern that indicates areas for closer study. With the Identify
tool you can determine the names of a few census tracts are ideal
candidates for your report.
Areas
with more than 75% sewer connections.
4. Processes
Both the natural and
urban environments change frequently in a number of ways. One of the
biggest challenges facing GIS is how to show these changes. Because most
GIS emphasize patterns, they also include functions for transforming
processes and relationships into patterns that you can analyze. (You will
learn about these later.)
a)
Processes
show changes in geographic relationships
Everything changes;
geography is no exception. Whether it is a landslide on a mountain slope,
centuries of silt deposits at the mouth of a river, or the migration of
people from cities to suburbs, geography deals with change. The most common
way to deal with these changes is to model the process.
There are numerous
application specific models for examining processes, and because they show
the dynamics of change they are invaluable. Maps, whether paper or digital,
can show the results of a process and the conditions before and during a
process. Being able to see how an area began and how it ended up can
provide helpful information when you have to make spatial decisions.
b)
Processes
connect patterns
Maps make it
possible to show the different stages of a process. For instance, if you
compare a map of Ouakam showing census district populations in 1990 and use
the same classification for a map of populations in 2000, you can see which
county populations have increased or decreased. The differences in the two
patterns indicate processes. A comparison of the two maps below shows the
population pressure especially in the western part of Ouakam. If you had
data for the ten-year period between 1990 and 2000, you could see more
details about these processes. Perhaps all units increased in population in
the early 1990s, but a few increased after 1996. The more data you have
available for the time period the more you can say about the processes.
Ouakam
Population 1990. The darker the color the greater the population.
Ouakam
Population 2000. The darker the color the greater the population.
Numbers indicates % population change.
The same type of
comparison can be used for other types of processes, both natural and
man-made. By examining street data for different years, you can assess an
area's highway construction. By comparing land use data from different
years, you can see where suburbanization has occurred. Some changes can be
readily detected if the changes last longer than the time interval between
data collection. For example, because a house is built to last at least 30
years, checking land use data sampled at ten-year intervals will give you a
good idea of suburbanization. But detecting changes in agricultural
practices with the same data will be more difficult, if not impossible.
Farmers may gradually move from sorghum to wheat if wheat stays more
profitable over time, but a five year boom in wheat production will be lost
if the only data you have is from before and after the boom period. Because
temporal comparisons are very sensitive to duration they are prone
to great deals of inaccuracy of not done properly.
c)
Accounting
for natural and human dynamics
Comparing patterns
still remains a common way to detect change. If done appropriately, it is
largely reliable and offers a powerful way for communicating changes that
people might otherwise overlook. Most people forget exactly when certain
changes occurred. As they get used to the changes, it often seems like
things have been a certain way for a long time. Geographic information
helps demonstrate change while it also helps people understand the magnitude
of these changes. GIS helps show people the natural and human processes
that are happening all the time.
Population
Change in Ouakam between 1990 and 2000.
Blue tones denote below average changes, red tones above average.
Another common use
of geographic information from different times is predicting changes. For
planners, politicians, and citizens, it is important to know what impacts a
construction project will have on water run-off. A common concern is
whether the existing sewer system will handle the increased run-off. If it
cannot, then an expansion of the existing network may be necessary. While
these types of analysis are not very accurate, they can help people grasp
the extent of changes. Although more exacting scientific methods are needed
to quantify the specific changes, comparing different views often offers a
good start
5. Example
a)
Reducing
flooding risk
New construction
leads to increased run-off potential and often increased flooding risk. If
precipitation isn't properly managed, property damage and harm to people
can result. Knowing which direction precipitation flows in the area of a
planned subdivision can help planners predict where new water retention
structures should be built. A digital elevation model (DEM) provides the
topographic data necessary to determine the locations of these retention
structures.
Developing flow
indicators may seem complex, but can be done in two simple steps. First,
the direction water flows from each DEM cell to other cells must be
established. This gives a very rough indication of how water will flow
following rain. Darker areas indicate where the water flows towards the
south or east because those areas are at a lower elevation. Lighter areas
are where the water flows to the north or west. There are a few places
where appropriate care needs to be taken to insure that rainfall does not
run uncontrolled and cause damage or harm.
Flow
direction in the planned subdivision.
Cumulative flow
indicates where precipitation accumulates, such as rivulets, streams, and
rivers. Water always flows downhill, so the DEM makes it possible to
determine where water will flow from cell to cell. By keeping track of the
theoretical cumulative amount of water as it flows down, the approximate
location of water bodies can be detected. Darker cells have a greater
amount of flow. In the area of the planned subdivision, it looks like most
water flows to the northwest. Based on calculations that predict the amount
of rainfall that can be expected, planners can decide where to build water
retention structures and how big those structures need to be. Although you
don't have that data, this GIS analysis will still be helpful in developing
the initial plans.
The pattern below
concludes the analysis and indicates the dynamic flow accumulation process.
The increasing darkness of the raster grid cells indicates how water will
flow. The change from lighter to darker colors indicates the theoretical
accumulation of rain water as it runs down into water bodies carrying
larger amounts of water.
Flow
Accumulation.
Summary: Comparing similar information
over a span of time can reveal patterns of change. The more data you have
available for a time period the more you can say about a process.
6. Exercise
a)
Examine
population growth
In this exercise you
will examine the process of population growth in Ouakam. You will see how
you can identify change by comparing different views
Step
1 Start ArcView and open the project
Start ArcView.
From the File menu,
choose Open Project. Navigate to the sdm\lesson1 folder and open the
project lab5-02.apr.
When the project
opens you see a view containing 1990 Census population data for each of the
32 census units of Ouakam.
Step
2 Examine the 1990 population for Ouakam
Click the Population
1990 theme. Using the Identify tool, click on each of the counties.
Examine the difference between the values in the 1990 and 2000 fields to
determine the population change between 1990 and 2000 for each census unit.
The changes are
about average in district 3, but far below average (though still a net
growth) in district 17, and some substantial growth in district 7. The
differences suggest that the areas in the South and East have reached a
certain degree of saturation (large population, little growth), while the
center of Ouakam may be filled with non-residential properties (little
population, little growth).
Close the Identify
Results window and click on the Zoom to Full Extent button.
Step
3 Examine views side by side
While it is possible to
identify change for a small number of units this way, putting two views
next to each other will make it possible to examine the patterns that
indicate changes for the study area.
Open the Population
2000 view by double-clicking its name in the project window.
To see both views at
once, you would have to move and position the two views until they both fit
in the ArcView window, making sure they were both at approximately the same
size and scale. An easier way to do this, and at the same time prepare a
map for printing, is to use ArcView's layout feature. In this case, a
layout has already been prepared for you.
Close both views.
Click the Layout
icon in the Project window and double-click on the Population Comparison
1990 - 2000 layout to open it.
The layout makes it
much easier to view differences in population between 1990 and 2000 both
for the entire state and for individual counties. Because of the way the
population data was classified, the census units appear to have changed
little. Overall Ouakam didn't really see many sweeping changes during that
decade.
Close the layout and
open the Population 1990 view.
Step
4 Analyze changes
Comparing views is a
good way to develop an overview of any changes that may have occurred. It
also helps to develop hypotheses about those changes. In this step, you
will check the accuracy of the hypothesis that population growth was evenly
distributed across all of Ouakam (a so-called null hypothesis).
Open the Attributes
for Population 1990 table. Because this table contains the 1990 and 2000
census data, you can calculate the change in population by subtracting the
1990 values from the 2000 values, then saving the results in a new field.
Click the column
header of PopChange. Currently its values are set to null for all counties.
From the Table menu,
select Start Editing.
From the Field menu,
choose Calculate.
Double-click the
Y2000 field, then the subtract operator, and finally the Y1990 field.
Click the OK button.
The values for the population change in each county appear in the Popchange
field.
From the Table menu,
select Save edits and then Stop Editing to keep these changes. Verify that
you want to keep the edits when prompted. Close the table.
Make the view active
and double-click the theme in the view's table of contents to open the
Legend Editor.
Change the
classification field to PopChange and the color ramp type to Grays to Reds
dichromatic. Click Apply and close the Legend Editor.
This view clearly
shows several important population changes not visible in the comparison of
population counts. Most striking is the change in the South, where
extremely low and high changes of population growth are right next to each
other. Altogether, there is a high degree of variability so the null
hypothesis about equal growth can be rejected. This then leads to the
question of “why”; what causes the differences? We will look for the
answers with the development of an Urban Indicators template in the
following section.
7.
Urban
Indicators Template
8.
Summary
Geographic
information systems make flexible analysis of geographic information
possible as never before. I this lesson you learned the basics of patterns
and processes that are the foundation for making sound spatial decisions.
Patterns are how people indicate geographic characteristics of the world
around them. Patterns show where things are and they help understand how
things are related. These relationships also represent processes. Processes
in a GIS are represented by changes in patterns. Spatial decision-making
relies on the flexibility of GIS to transform patterns in almost limitless
ways that you will learn about in the other course modules.
