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A
DETAILED DESCRIPTION OF
THE
UPLAN MODEL
Robert
A. Johnston and David R. Shabazian
Dept.
of Environmental Science and Policy
University
of California, Davis, CA 96161
rajohnston@ucdavis.edu
July,
2001
TABLE
OF CONTENTS
(Headings
are links)
A.
UPLAN
1. STRICT
COMPLIANCE
2. LIMITED
COMPLIANCE
3. INDUSTRIAL
COMPLIANCE ONLY
4. NO COMPLIANCE
B. UPLAN DATA
1. ATTRACTION GRIDS
2. EXCLUSION GRIDS
a. slope
discouragement
UPLAN is a GIS-based expert decision rule urban growth model that was
conceived by Robert Johnston at UC Davis and built by David Shabazian.
Its original purpose was to disaggregate the zonal output of urban
models such as TRANUS and MEPLAN to specific locations within each
analysis district. However,
UPLAN was eventually fitted with a demographic/land consumption module
that generates demand for land endogenously from user-specified
parameters. Its structure is
most similar to that of CUF, but employs user-specified attraction weights
rather than potential profit as the main factor driving the allocation
routine. UPLAN also differs from CUF in data structure: it uses 50-m2
raster data because it requires less disk space, and model run time can be
substantially reduced as cell size increases (data sets also become
smaller). There is a tradeoff
however, because larger cells mean less detailed allocation results.
This issue of resolution adds another dynamic to the model that can
be adjusted to fit the needs of the user.
The model runs on a PC in ArcView, a common desktop GIS application
that is used by most planning and natural resource agencies.
The data required to run the model are generally available from
regional or state resource and planning agencies.
Because UPLAN uses complementary software and data, it can easily
become a “spatial process model” for agencies that already have
“spatial data models” in place.
UPLAN uses 1990 as its base year
and allocates the increment of additional land consumed in future years.
This increment of urban growth is in discrete land use categories:
Industrial, High Density Commercial, Low Density Commercial, High Density
Residential, Medium Density Residential, and Low Density Residential.
One method of estimating the incremental growth uses the land
consumption projections from TRANUS or MEPLAN.
These integrated models provide estimates of land consumed in 53
zones that are then fed into UPLAN to be allocated to specific locations
within each zone.
County or regional land consumption
can also be calculated endogenously on user-specified assumptions thereby
allowing one to run the model independently. UPLAN is built with a module that allows the user to input
demographic and land use factors that are converted to acres of land
consumed in each activity class. The
conversion starts with population projections for counties or the entire
region. The user is then
prompted to specify the demographic and land use characteristics he or she
would like to test. To
determine acres needed for future housing, the user specifies persons per
household, percent of households in each density class, and average parcel
size for each density class. A
similar conversion is used to determine acres of land consumed for
industry and commerce and uses workers per household, percent of workers
in each employment class, and average area per worker (in acres).
Both methods of deriving future land consumption produce a table
from which the model operates its land allocation routine.
It is assumed that development
occurs in areas that are attractive due to their proximity to existing
urban areas and transportation facilities, such as highway ramps or light
rail stations. It is also
assumed that the closer a vacant property is to an attraction, the more
likely it will be developed in the future.
For example, a property that is a quarter mile away from existing
development (or any attraction for that matter) is more desirable then one
that is a mile away from the same location.
Following this crude bid-rent assumption, each development
attraction (described below) is surrounded by user-specified buffers.
The user can designate the number and size of the buffers and
assign an attractiveness weight to each buffer.
Buffer specifications are applied to each of the attraction grids
and then the grids are overlaid and added together to make a composite
Attraction Grid. The
composite Attraction Grid is a single grid of the sum of the weights
specified for each individual attraction grid.
Each cell in this grid has a value resulting from the summation.
Grid cells with the highest value are considered the most
attractive areas for development while cells with a value of zero are
considered the least attractive areas.
Naturally, there are areas where
development cannot occur, which are called exclusions.
Exclusions include features such as lakes and rivers, public open
space, existing built-out urban areas, and other such areas where
development cannot occur. There
are also features that the user can choose to exclude, such as sensitive
habitats, 100-year floodplains, and farmland.
Once the user decides which features are to be excluded, the model
adds together the various exclusion grids to generate a “Mask.”
Like the composite Attraction Grid, the Mask Grid is a sum of the
individual exclusion grids. In
this case, however, grid cell values are not important per se; rather,
simply that a cell has a value makes it part of the Mask.
Once the Attraction Grid and the
Mask Grid are generated, the model overlays the two grids and attraction
cells that fall within the mask are converted to “no data” cells,
thereby removing them from possible development allocations.
This process creates the Suitability Grid, which becomes the
template for the allocation of projected land consumed in the future.
The Suitability Grid is overlaid with a grid of the 20-year General
Plan land use map for the region enabling the model to further isolate
areas which are suitable for each of the various land use categories that
are allocated. The model is
then ready to allocate projected acres of land consumed in the future.
There are four ways in which this allocation occurs
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1.
Strict Compliance – Each activity can only
be allocated to its corresponding designations in the General Plan as
follows:
Activity Type General Plan Category
Industry
→
Industry
High-Density Commercial →
Commercial
High-Density Residential
→
Multi-Family Residential
Low-Density Commercial →
Commercial
Medium-Density Residential
→
Single Family Res., Urban Reserve
Low-Density
Residential →
Single Family Res., Urban Reserve and
Rural Residential
This is called “two-way zoning,” legally.
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2.
Limited Compliance – Each activity is
allocated to its corresponding General Plan designation and to the
designations available to the previously allocated land use. Activities are listed in order of allocation.
Activity Type
General Plan Category
Industry
→
Industry
High-Density Commercial
→
Industry and Commercial
High-Density Residential →
Ind., Comm., and MF Residential
Low-Density Commercial
→
Industry and Commercial
Medium-Density Residential
→
Ind., Comm., MF Res., SF Res., UR
Low-Density
Residential
→
Ind., Comm., MF Res., SF Res., UR,
and
Rural Res.
This is called “one-way zoning,” legally
3.
Industrial Compliance
Only - Industry must go to
industrially designated areas, and all other land uses can go anywhere.
4.
No Compliance – All land uses are allowed to
go into any land use designation.
The model allocates future
development starting with the highest valued cells. As the higher valued cells are consumed, the model looks for
incrementally lower valued cells until all acres of projected land
consumption are allocated. The
model does this for each of the discrete land use categories and the user
can decide the order in which land uses are allocated and to which general
plan categories they can be allocated.
By default, the model starts with industry, then proceeds to
high-density commercial, high-density residential, low-density commercial,
medium-density residential, and finally low-density residential.
This order is chosen to represent the way in which the land market
typically operates - higher valued land uses are more competitive in
acquiring the most desired properties thereby outbidding the less valuable
uses. The allocation routine converts future acres consumed to the
number of cells needed. It
then determines how many cells are available in the highest valued
category and if this is less than what is needed, simply converts all
those cells to the designation of the land use it is allocating at that
time. It then subtracts the
number of cells it just allocated and moves on to the next highest cell
value and again determines how many cells are available.
When the model reaches a point where the cells available are
greater than needed, the model completes its allocation of that particular
land use by randomly allocating the remaining development to cells within
the current value class.
This allocation method does not
apply to low-density residential, which is randomly allocated throughout
rural areas to represent the prevalent noncontiguous patterns of exurban
rural residential development, such as hobby farms.
Because the allocation is random, low-density residential does not
use the Attraction Grid to find the best locations; however, the Mask Grid
does apply. The low-density
residential allocation routine starts by making a generic grid of random
values. It then makes a list
of the values and allocates, in descending order, to the random cells
until all acres of low-density residential land are used.
While all other land uses are allocated in 50 meter grid cells
(≈ ½ acre), 200 meter grid cells are used for low-density
residential to represent average parcel size (≈ 10 acres).
After a land use is allocated for
all zones, counties, or for the region, the model makes a new grid of that
allocation. This grid is
saved in the working directory, but also added to the Mask Grid so that
the next land use being allocated does not overlap previous allocations.
Once the model has allocated all the land uses, it merges all of
the allocation grids it has created to make the final Allocation Grid: a
grid that has the allocation of all land use types in all zones, counties,
or the region as projected out to the year tested.
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Most of the data listed below would typically be used in any
region where UPLAN is applied. For
example, any application of the model would require basic attractions,
such as cities and sphere of influence or
freeway ramps, or basic masks,
such as waterbodies or existing urban development.
There are also attraction and exclusion characteristics that are
specific to a region. For
example, in the Sacramento region there is an international airport that
is used as an attraction, and there are unique geological features called
vernal pools that can be used as exclusions. The data used for the application of UPLAN to the Sacramento region
are described below.
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1. Attraction Grids
As mentioned above, there are a number of attraction
grids that are used to create the Composite Attraction Grid that
identifies where development will most likely occur in the future.
The user can specify a number of buffers, the size of each buffer
and the weight or attractiveness of each buffer.
Generally, higher weights are assigned to the buffers that are
closer to the source. This
section provides a brief description of each of these grids.
1.
Freeway Ramps – This grid represents the
location of on- and off-ramps on major freeways, rather than the entire
facility. Since major
freeways have limited access, ramps are the most desired locations for
development along these facilities. This
grid allows one to test the effect of changes in the facility by adjusting
the weighting of the ramp buffers. For
example, if the facility were improved with carpool lanes, one would
increase the weights on ramps to reflect the additional capacity, which
would in turn make property around ramps more valuable/attractive.
2.
Minor Highways – These highways are
generally two lane roads that are intersected by county roads and other
major arterials. Therefore,
they are considered to have enough access to use the entire facility as an
attraction. As with other
facility improvements, if minor highways are changed, the user can adjust
the weights and buffers to reflect to these changes.
3.
Major Arterials – These are also two lane
roads, but generally have lower speed limits and are much shorter in
length. These roads have even
more access points than minor highways; therefore the entire road is used
as an attraction. Buffers and
weights can be changed at the user’s discretion as with other attraction
grids.
4.
Minor Arterials – This is the least
important roadway type used in the model.
Speeds are generally slower than major arterials and there are
numerous intersections with other arterials and local roads (which are not
used in the model).
Because the roadway classes are in
separate grids and then added together, intersections between roads
receive a higher weight because of the occurrence of two or more roads.
This provides a fairly realistic representation of the real world
in terms of where developers look for property to develop. By default,
roadways with more volume (i.e., Highways) have higher weights than
arterials, however, the user may choose to specify different buffers and
weights. All road data were
derived from U.S. Geological Survey (USGS) digital line graphs (DLG) for
transportation.
5.
Cities and their Spheres of Influence
– This grid is a representation of the existing boundary that defines a
city. The boundaries used in
the model are not city limits, but rather the city and the area around it
where development will most likely occur in the future – the Sphere of
Influence. The data were
provided by SACOG (Sacramento Area Council of Governments, the region’s
Metropolitan Planning Organization).
6.
Light Rail Stations –
This grid represents the location of existing and or future light rail
stations. The rail line
itself is not used as an attraction since development activity would most
likely be concentrated around the stations.
Using this grid one is able to test scenarios with varying degrees
of emphasis on Transit-Oriented Development and transportation policies
favoring rail transit. The
data were generated by the author by digitizing SACOG maps of existing and
proposed light rail lines.
The following grids are used in addition to the
aforementioned data, for industrial allocation only.
These grids are not used for the allocation of the other land use
types because these ports are surrounded by industrially designated land.
This land use policy may not apply to all regions where UPLAN might
be used and the model can be modified to reflect any such changes.
7.
Sacramento International Airport
– The airport is currently in the process of expansion; therefore, the
model accounts for the likely buildout of the surrounding areas by
including this grid as an industrial attraction.
The data were digitized by the author using California State
Automobile Association maps of the region.
8.
Port of Sacramento – The port and its
surrounding area has traditionally been an industrial district.
While it is more developed than the airport, there are still areas
that can accommodate future industrial development. This grid was also
generated by the author using California State Automobile Association maps
of the region.
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2. Exclusion Grids
Once
the attraction grids are combined to create the Composite Attraction Grid,
the following exclusion grids are combined to create the Mask Grid:
1.
Regional General Plan
– This grid is a composite of all the general plan land use maps for
cities and counties in the study region.
SACOG constructed this grid by merging all the individual general
plans and then generalizing the land use designations across the region.
The model uses this grid to find locations that are appropriate for
each land use category it allocates.
This restriction depends on the type of compliance chosen by the
user and converts all cells that are not “in compliance” to “no
data” in order to remove them from possible allocation.
For example, if the user chooses to have strict general plan
compliance, then the allocation of commercial land is restricted to
commercially designated locations as determined by the regional general
plan.
2.
Rivers – The model uses the
“Naturally Occurring Waterways” grid as the river exclusion for this
model. This grid was
generated from the EPA River Reach files by the Information Center for the
Environment (ICE) at UC Davis. The
EPA data originates from the USGS hydrography DLGs.
The grid includes all major and minor waterways for the study
region, but does not include ephemeral streams or man-made waterways such
as channels and canals. A
user-specified distance buffers the rivers before they are added to the
Mask Grid. This precludes
development from occurring too close to waterways.
3.
Lakes – This grid contains all of the
water bodies in the study region. As
with rivers, all lakes and ponds are buffered by a specified distance
prior to being included in the Mask Grid.
The data were generated from the USGS hydrography DLGs as modified
by the U.S. Department of Fish and Game.
4.
Vernal Pools – Since many vernal pools are
small and difficult to represent in a digital format, a grid of vernal
pool “complexes” created by the U.S. Fish and Wildlife Service is
used. There are four types of
complexes that the user can choose as exclusions: High Density, Medium
Density, Low Density, and Cultivated.
5.
Floodplains – This grid contains the 1996
100-year floodplains for the study region as determined by FEMA.
This grid can be used to test how development patterns might change
due to the implementation of land use policies that prohibit development
in floodplains. This grid can also be used to evaluate the potential damage
and financial impacts of building in the floodplain.
6.
Slope
–
Slope is used to mask out areas that are too steep to develop, and as a
discouragement factor for areas that remain.
Slope is derived from Digital Elevation Models (DEM) provided by
the USGS. The discouragement
factor works by dividing the Attraction Grid weights by the values shown
in the table below. This is
done for each land use type before the allocation begins.
The slope and mask restrictions are as follows:
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Ind., Comm. High,
% Slope
Comm. Low, Res. High
Res. Med.
Res. Low
0-5
1
1 1
5-10
2
1 1
10-15
3
2 2
15-25
mask
2 2
25-50
mask
3 3
50-100
mask
mask
3
100 +
mask
mask
mask
7.
Public Lands – This grid includes all of the
areas designated as publicly owned land in the SACOG General Plan grid for
the region. The author
extracted the “Public-Owned” designated grid cells from the General
Plan to create this grid. Public
lands include areas such as schools, military bases, and forestlands.
Generally, these lands should be excluded from development, except
where military bases are converted to private uses.
8.
Fire Severity – There are three levels of
potential fire severity that can be used to exclude development: Very
High, High, and Moderate. The
ratings were generated by the California Department of Forestry and are
based on characteristics such as fuel loading, slope, fire weather, and
other factors. As with the
Floodplain grid, the fire severity grid can be used to assess the
potential loss due to fires.
9.
Existing Urban – This grid was
constructed using data from the Department of Fish and Game and the
Department of Forestry. Each
agency has 30 meter grid data of existing urban development that is
generated from satellite imagery. Urban
designations are distinguished from water and vegetation by their spectral
value. While the
interpretation of the satellite data does produce some false positive
cells (an urban designation where there is no urban) and false negative
cells (no urban designation where there should be), the grid allows us to
test infill and redevelopment policies at a detailed scale.
10.
Open Space – There are “public open
space” areas designated on SACOG’s Regional General Plan that are used
as an exclusion. This
designation includes parks, greenbelts, wildlife preserves, and other
areas set aside for recreation or preservation.
11.
Farmland – Currently the “agriculture”
designation in the regional general plan is used as the farmland exclusion
grid. The user is allowed to
exclude these agricultural lands from development if desired. This coverage can also be used to determine how many acres of
farmland are being converted for future urban uses. A data set provided by the Department of Conservation,
Farmland Mapping Project that delineates four types of prime farmland can
also be used. This grid
allows the user to specify which types of farmland to exclude, and/or
conduct more detailed assessment of farmland conversion for urban uses.
12.
GAP Vertebrate – The GAP data is used to
determine which areas in the region have the richest concentration of
species. Currently, the model
uses the top 10% richest areas for each species type: amphibians, birds,
mammals, and reptiles; however, the user may change the percent richness
to test various policies. This
grid can be used as an exclusion or as an overlay to determine where and
to what extent urban development encroaches on sensitive habitat.
The data were originally generated by the UCSB GAP Analysis
Project.
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