Statewide Land Use Layer Data Assessment ‐ Phase 2 Project Report Statewide Land Use Phase 2 ‐ Project Summary.docx 1 Statewide Land Use Layer Data Assessment Phase 2 Project Report This project report is a description of work performed during the period of January through June of 2017 by Lane Council of Governments (LCOG) with funding from the Oregon Department of Land Conservation and Development (DLCD). Executive Summary Based on the findings of prior work performed for DLCD in 2015, LCOG re‐examined statewide taxlot data for its usefulness as a primary input in the development of a statewide land use layer. Information regarding potential use cases and data requirements was gathered from stakeholders at DLCD and other state agencies. Alternatives to the previous approach (which was based on the statistical class or building type) were explored and it was found that there were several ways to significantly improve those earlier result and better meet the anticipated data user needs and use cases. The primary change in approach was to use a different county tax assessor data field, known as property class. Resulting land use classifications were similar to earlier results for some types of lands, but resulted in better classification of other lands, especially in rural areas. A statewide land use coding scheme was developed based on property class. The coding scheme was based on the detailed review of five focus counties from various parts of the state. The coding scheme was applied with some success to several additional counties. The coding scheme will be expanded to include additional counties as part of statewide implementation. Another improvement was the integration of public land ownership and management data from Oregon Department of Forestry (ODF) and the Bureau of Land Management (BLM). This allowed for better classification of rural public lands, especially in those counties which do not identify public ownership through the property class. Other potential improvements were considered using data that may be available in the future from Oregon Parks and Recreation Department (OPRD) that could someday improve the classification of public lands in urban areas (such as parks and schools). A statewide approach based on acquisition of taxlot data in a county‐by‐county fashion would be costly both in terms of time to acquire and to process the data. Fortunately the ORMAP program under the administration of the Oregon Department of Revenue (DOR) provides a source of taxlot and property class information in a standardized format and on an annual basis. While there are some issues with the availability (a handful of counties do not participate in ORMAP), completeness, and consistency of that data, it is a cost‐effective starting point. Starting from the ORMAP data, LCOG developed a proposed implementation strategy, inventoried most of the issues that will need to be addressed, and produced preliminary statewide dataset. LCOG also developed recommendations for ongoing stewardship and maintenance of this data by DLCD. The stewardship and maintenance plan recommends that DLCD work to foster partnerships that would create data improvement feedback loops to DOR and the counties, as well as to ODF, BLM and other partner agencies.
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These classification systems must also be adaptable over time to inevitable changes in human activities,
evolving data collection methodologies, and varying needs for data currency and specificity.
Ultimately, and in practical terms, the goal of the LU/LC FIT Work Group is to find workable solutions
that can supply needed data about land use and land use change. Use cases for the data will likely need
to be lumped or split to take advantage of the variable resolution, specificity, and timeliness of available
data sources. Combinations of broad remotely sensed land cover, parcel‐based uses, and detailed
structure or facility features will all be useful for statewide users of land use information.
Terminology
• Land Use is the current use of a site or property (typically at a parcel or sub‐parcel level). The data is usually in vector format, includes information that can’t be reliably determined from imagery, requires supplemental information about human activities, and generally offers less detail in rural areas and more detail within urban areas.
• Zoning refers to the use allowed by local or other authorities on specific parcels of land. Zoned use is not always the same as the existing use, and may reflect historical or anticipated future use. In other words, land is not always used in the way it has been zoned. Additionally, a particular zone may allow for a number of different uses that are considered compatible with the intent of the zone.
• Plan Designation is the long‐term ideal or desired future land use for a specified land area, and expressed in a comprehensive plan which is intended to guide development according to adopted policy.
• Land Cover is typically a raster representation of the Earth’s surface based on biological and/or physical characteristics derived from remotely sensed data, such as “broad leaf forest” or “impervious surface”. It traditionally offers very little detail of land use inside urbanized areas, and is generally more useful for landscape‐scale analysis.
SummaryofFindings
1. There is a wide range of current and potential use cases for a statewide GIS layer of Land Use.
Numerous state agencies, as well as local and regional governments, have a need for Land Use
information that cannot be entirely met by the use of zoning, land cover, or other existing data layers. A
number of use cases are described in the matrix below, along with specific comments related to special
attribution requirements, and comments related to geometry, spatial resolution, and currency
requirements (update cycles).
2. Some use cases would benefit from having a layer of structures or building footprints.
Some use cases have specific data requirements which would be better met by having a layer of
structures or building footprints, perhaps in addition to a layer of land use polygons. For example,
detailed information about structure type and occupancy is critically important for damage and loss
assessments related to natural hazards. While a structures or building footprints layer is well beyond the
scope of this current phase of work on land use data development, it should be noted as something to
work toward. Structures or building footprints could someday be used in conjunction with parcel‐level
Task2:Refinement/ImprovementofLandUseCodingAlgorithmsBriefly, Task 2 included the following activities:
Worked with taxlot data acquired directly from five Oregon counties to develop a Land Use classification scheme built primarily on Property Class,
Refined and improved the classification scheme by using a composite approach which incorporates an existing statewide data layer depicting public lands ownership,
Convened 3rd Work Group meeting to review results and discuss next steps.
IntroductionWhile highly informative, the 2015 Phase 1 results, along with subsequent feedback received from
members of the Work Group, brought to light a number of challenges and shortcomings associated with
crafting a land use coding scheme based solely on Stat Class, including the following:
The many‐to‐one relationship that can exist between Stat Class and taxlots (due to presence of multiple buildings or other improvements).
No characterization of undeveloped taxlots, which carry no Stat Class information.
No clear distinction between rural residential and urban residential use.
Lack of statewide standardization of a Stat Class coding scheme or data structure. For this current Phase 2 of the effort to develop a statewide Land Use data layer, LCOG has focused on
the use of property class (hereafter referred to as “Prop Class”), as used by various county tax
assessment offices around the state, to derive Land Use polygons at the taxlot level. While it comes with
its own set of challenges, using Prop Class as a basis for developing a land use coding scheme does
address each of the shortcomings mentioned above with regard to Stat Class:
Each taxlot generally carries (by definition) a single Prop Class value.
Developed and undeveloped taxlots carry Prop Class information, and Prop Class codes often indicate presence or absence of improvements.
Prop Class provides some distinction between rural residential and urban residential use.
Property Class is defined by Oregon Administrative Rule and application of it is relatively standardized statewide (see below).
It is worth noting, however, that while a land use coding scheme based primarily on Prop Class can help
address some of the challenges and shortcomings inherent in a Stat‐Class‐based approach, there are
more detailed levels of land use information that can be provided only by characterization at the
structure level, such as Stat Class provides. Nevertheless, at this time a structure‐level or sub‐taxlot
determination of land use does not appear to be feasible within practical constraints of time and
budget. Through other FIT groups, the state is making progress in related areas, such as identification
and mapping of critical facilities, compilation of site address points, and possibly compilation of building
footprints, which may someday provide a feasible pathway to structure‐level or sub‐taxlot
determination of occupancy or use.
Taking the Work Group input from Task 1 into consideration, LCOG improved upon the June 2015 land
use categorization in a number of significant ways:
A. The Tax Lot Property Class (Prop Class) was utilized to achieve better results on undeveloped lands, where the Stat Class‐only approach did not work as well.
B. Additional land use categories were created for the urban fringe and lightly‐developed rural areas, including Rural Residential , Agriculture , and Forest.
C. Other existing statewide datasets were examined which could be used to more accurately
reflect existing land use in certain specific areas or situations. During Task 2, LCOG developed these improvements in methodology through application to the same five Oregon counties for which maps were prepared for the June 2015 Phase 1 report, using the same base data:
Deschutes County
Harney County
Josephine County
Lane County
Multnomah County
Discussion
PropertyClassasUsedinOregonProperty Class, as applied by tax assessors in every county in Oregon, follows a relatively (but not
universally) standardized three‐digit approach, as shown in Figure 1 below.
classification process, and the resulting Land Use classifications are completely independent of the
statewide zoning layer.
The following paragraphs describe some of the data‐related challenges encountered during
development of a consistent Prop Class‐based land use classification system.
DataStructureandNon‐TaxlotFeaturesIn Harney County, numerous instances were found where taxlot polygons carried a Prop Class code with
a trailing space, which meant they would not successfully join to the master lookup table. One possible
approach would be to simply extend the master lookup table to include every Prop Class value both with
and without a trailing space, but this would be overly redundant and it is possible that instances of more
than one trailing space could be encountered. Instead, a pre‐processing step can be added to remove all
trailing spaces from Prop Class codes. Pre‐processing steps such as this will certainly have to be
integrated into statewide implementation and ongoing maintenance.
The taxlot data obtained from some counties needs to be filtered in certain ways to screen out non‐
taxlot features and/or redundant “placeholder” features. In Harney County, for example, non‐taxlotted
areas were screened out with a Definition Query based on the “Taxlot” field. In the taxlot data obtained
from Multnomah County, the attribute field “Tl_Type” was found to have five possible values, and only
those records where TL_Type = “TAXLOT” were needed. The other “types” belong to subaccounts or
easements and are attached to replicate features. In Deschutes County, Prop Class is either blank or
coded as “000” on a large number of features, far more than in other counties examined. Some of these
features appear to be private roads while others appear to be common area around condos, but in
many cases it is not obvious why these taxlots would not have some other Prop Class or would be
considered unbuildable.
Note that some of these county‐specific data structure and format issues could be avoided if a
standardized dataset could be acquired for all counties. In fact, the standardized taxlot datasets
delivered by most Oregon counties to the Oregon Dept. of Revenue (DOR) as part of the ORMAP
program do include an attribute field for the Prop Class codes (as part of a table submitted with the
taxlots). The most feasible approach to statewide implementation likely relies at least in part on the use
of these ORMAP submissions to DOR (discussed in more detail under Task 4 below).
PropClassesAppliedtoNon‐Farm/Non‐ForestRuralLandsAll five counties examined for this current phase of work, and presumably all counties in Oregon, use the
somewhat‐generic “Tract” category of Prop Class (4xx) for large numbers of rural taxlots. For the most
part, these tend to be in rural residential use, although there are some exceptions.
ResidentialCondominiumsWhether taxlots with condominiums should be considered as single‐family residential or multi‐family
residential uses seems to depend largely on setting and surroundings, but a consistent classification will
greatly simplify statewide implementation. At this time, residential condos have been classified as multi‐
family residential, based on the predominant observed usage.
Findings–InitialLandUseClassificationResultsThe land use classifications initially used for Prop Class based assignments are shown in Figure 2. Several
new classes were added to those used in the Stat Class based classification (Phase 1), informed primarily
by Work Group discussions around potential use cases, and in order to meet the need for better
characterization of rural and semi‐rural lands in particular. Providing separate classes for developed and
undeveloped lands (in most categories) gives the end user the
flexibility to decide whether or not to lump all “undeveloped” lands
together, for example, or whether to lump commercial lands
together regardless of development status. (Keep in mind that
“development status”, in this context, depends on the Prop Class
assigned to each taxlot by the county assessor’s office, and can be
misleading. In the case of a large industrial facility, for example,
significant improvement value may span multiple taxlots, but all of
that improvement may be associated with just one of the taxlots.)
This initial Land Use classification scheme was applied to each of
the five “focus” counties, and compared to the Stat Class‐based
Phase 1 results for urban, rural, and transitional areas in each
county. These initial results are illustrated by the maps on next
page showing Deschutes County and Lane County (Figures 3 and 4).
At this stage of work, all publicly‐owned lands were lumped
together into a single classification. The fact that Lane County
makes no use of the 9xx (exempt) Prop Class codes is immediately
apparent. Recall that the same is true in Multnomah County, and
was later found to be true in other counties, as well. This
inconsistency in practice poses a significant challenge to the
development of a consistent statewide land use dataset. In the
following section of this report, other statewide datasets are
considered, and a possible solution to this issue is presented, which
also resulted in a refinement of the initial land use classification
ownership attributes in the ORMAP data could be used to identify public properties, but this would take
significantly more effort and require familiarity with the details of each county’s ownership information.
StatewideLookupTableAs described above, the work done during Task 2 resulted in development of a single master lookup
table, which was applied consistently to all five focus counties. This lookup table is included as Appendix
B to this Project Report, and provides a common scheme that can be extended to other counties. A
single table was developed instead of a table for each county for two primary reasons:
In reviewing the data for the five focus counties and the statewide data found in the ORMAP
data, it was observed that counties appear to be using most of the common codes in generally
similar ways.
A statewide table is a cost‐efficient initial choice and there is also the option to develop
exceptions tables for some counties if the statewide table becomes too restrictive.
LCOG staff found some variation in how Prop Class data is coded across counties. As might be expected,
a few counties use coding schemes that are unique to them, while most others stick closely to the codes
as defined in the OAR, but with some variation in usage.
It was not difficult, in most cases, to assign land use codes to Prop Class codes. This is partly because the
land use classification scheme that has been developed is somewhat generalized. So far, unusual or
“outlier” usages of Prop Class have been accommodated within the lookup table. An alternate approach
would be to build a separate table for each county, as was done for the Stat Class work (Phase 1).
However, the Prop Class coding scheme is standardized to the extent that these county‐specific tables
would be largely identical. Another approach might be to build a single master table and apply it to all
counties, but then make use of county‐specific “exception” tables to re‐assign particular Prop Class
codes which are used in a given county in some way that doesn’t conform to the standard adopted by
most other counties. Use of these exception tables would need to be thoroughly documented, and
could complicate the process of constructing legends and layer files. The choice of one approach over
another may depend on how many significant exceptions are encountered among Oregon’s 36 counties,
or it could come down to making a fundamental decision about whether or not to impose a consistent
land use assignment, despite some variation in how some Prop Class codes are used from county to
county.
ApplicationofStatewideLookupTabletoOtherCounties In order to test how successfully the lookup table derived from the five focus counties can be applied to
other counties, the table was joined to the ORMAP data for several additional counties and then
augmented with the ODF/BLM data for public lands, in accordance with the “composite approach”
described under Task 2. Some of those results are shown below in Figures 8 through 10.
A. This Project Report and Appendices including a. summary of agency needs and use cases b. description of classification methodology c. the expanded land use categorizations, d. master lookup table, as Appendix B e. maps of sample areas, as Appendix C
B. Statewide Implementation Plan
C. Preliminary Data Stewardship/Maintenance Plan D. An archived set of resulting GIS data including tax lots for the five counties with the enhanced
and expanded land use categorization applied, along with the Lookup Table itself.
DESCRIPTION OF USE CASE, APPLICATIONS OF LAND USE DATA
COMMENTS ON DESIRED ATTRIBUTION
COMMENTS ON DESIRED GEOMETRY AND RESOLUTION
DRIVERS OF REFRESH CYCLE
Natural Hazard Mitigation Plans
DLCD Cities Counties DOGAMI
Need to define change in development(i.e., change and trends over time). Land use baseline to identify where structures are being developed relative to hazard areas. Longitudinal data and baseline are needed for the next State Plan in 2020.
Building type, URMs, otherconstruction types.
Changes in land use, development trends.
Presence of dams/reservoirs.
Building Footprints would beideal
5‐year NHMP update cycle
Risk Assessments
DOGAMI DLCD OEM DEQ Cities Counties
Damage and Loss estimations at theindividual building level, using FEMA’s Hazus risk assessment software. Integration of building footprints, address points, and Assessor data (among other sources). Hierarchical coding schemes for scaling land use data across varying levels of specificity. For example, the distinction between two forms of residential uses—mobile homes versus stick‐built homes—used in Hazus to clarify loss impacts.
Some types of risk assessments,e.g., HAZUS EQ and Flood, require detailed information about each building, more than can be gleaned from assessment records.
Building class (military, public owned, religious, etc.), occupancy type/land use, specific occupancy (multi‐family, mobile home, etc.), year built, building material, foundation type, square footage, value, replacement cost.
Haz mat storage.
Individual Buildings. In somecases, if building information is not available, the assessment cannot proceed.
Refresh cycle may depend in part on the specific type of hazard being assessed.
Buildable Land Inventories
(aka Buildable Land Studies)
DLCD Metro COGs Cities Counties
Provides basis for determining availabledevelopment capacity, need for UGB expansion. Used in conjunction with constraints data, zoning/plan designation.
Existing uses. Key information isbuildable vs not buildable. Redevelopment analysis requires additional information. Need information about Ag uses in potential UGB expansion areas.
Vacant and Partially‐vacant lands.
Institutional ownership.
Sub‐parcel in order to identify,quantify, and spatialize partially‐vacant taxlots, which are especially important.
Periodic Review
Transit System Planning
ODOT MPOs Cities
What are the land uses in proximity totransit lines or proposed transit lines. Used to establish baseline, or to plan transit system expansions.
DESCRIPTION OF USE CASE, APPLICATIONS OF LAND USE DATA
COMMENTS ON DESIRED ATTRIBUTION
COMMENTS ON DESIRED GEOMETRY AND RESOLUTION
DRIVERS OF REFRESH CYCLE
Transportation Modeling and Planning
ODOT MPOs Cities?
Used to model trip generation andattraction, housing types and employment, generally aggregated to TAZ (Transportation Analysis Zone). Also used to constrain land use allocation forecasts for future transportation network. Zoning is sometimes used now as proxy for existing land use.
Surface parking is of particularinterest. Need to break out different types of Institutional Land Use, and ideally to break out vacant institutional land. Some institutional uses are considered high trip generators/attractors, such as hospitals and medical centers. Ideally would include data on Floor Area Ratio (FAR).
Parcel or sub‐parcel Polygons.
Building footprints.
Points (e.g., attribute of site addresses)
TSPs ‐Transportation System Plans
RTPs ‐ Regional Transportation Plans
Stormwater Modeling and Permitting
Cities DEQ
Often associated with Stormwater permitrenewals. Provides basis for applying impervious surface factors, and for modeling runoff generation and characteristics arising from interaction with land use.
Relatively general categories usedin runoff models. Zoning is commonly used now.
Best Management Practices
Low impact development, Green
streets management plans
Development runoff, Construction
permits.
Parcel or sub‐parcel Polygons.Need to also characterize ROW.
5‐year permit renewal cycle
MS4 discharge permit cycle
Infrastructure Capacity Assessments
Facilities Planning
Cities Utilities Metro COGs
Provides basis for quantifying existinghousing units and estimating potential future housing units (and other types of users?). Used in conjunction with constraints data, zoning/plan designation.
Provides basis for allocation of resources.
Boundaries of Utility systems.
Future Locations of Wastewater
Treatment Plants
Need for cross‐jurisdictional county‐level analysis
Utility Master Plans
Public Facilities Plans
Resource Allocation
Land Consumption Monitoring
Metro Others?
Annual vacant land inventories datingback to the 1990’s. Very good single family data, industrial lands, and multi‐family data at the sub‐taxlot level (drawn from Assessor’s data, imagery interpretation and field work). Maintaining reliable data on commercial uses is more problematic.
Conversion of industrial to commercial uses.
Have used Assessor Property Class to derive land use.
DESCRIPTION OF USE CASE, APPLICATIONS OF LAND USE DATA
COMMENTS ON DESIRED ATTRIBUTION
COMMENTS ON DESIRED GEOMETRY AND RESOLUTION
DRIVERS OF REFRESH CYCLE
Land Use Change Tracking
Protection of Resource Lands
DLCD Agencies have traditionally usedfarm/forest land cover data for rural areas, but need to track conversion to non‐resource use, and need to track land use changes in ways that go beyond the classifications typically used for property tax assessment.
Mix of rural residential with small‐scale ag, as distinct from rural residential without ag, and from rural residential accessory to ag as primary use.
Statutoryrequirements?
Crop Type Studies
Ag Land Impact Assessments
ODA
DEQ
Digitization of fields and attributing croptypes from statewide imagery. Used to assess upstream aquatic impacts.
Buildings/structures are not being mapped.
Crop types, crop rotations.
Ag practices, tillage, nutrient and pesticide management, BMPs.
Rural Residential with some small‐scale Ag use. Breaking out animal vs crop is a key distinction re: potential impacts.
Mixed uses on ag lands could be addressed in part through the classification scheme, e.g., Rural Res > 5 ac vs Rural Res < 5 ac., etc.
Urban fringe is particularlyimportant. Sub‐parcels are needed to fully describe mixed uses on ag lands. There may be need in rural/range lands for greater resolution in the land use classes, similar to urban lands.
Provides basis for evaluating potentialthreats to natural areas and habitats of concern, such as wetlands, fish‐bearing streams, etc. Used in conjunction with wetland inventories, other data pertaining to habitats and natural resources.
Conversion to non‐resource uses.Ag use and crop rotation. Conservation easements. Riparian areas. State‐owned lands.
System Development Charges (SDCs)
Revenue Calculations
Cities Water rates that depend on land use.
Brownfields Redevelopment
DEQ Cities
Identification/location of candidate sites. Historical land use information
DESCRIPTION OF USE CASE,APPLICATIONS OF LAND USE DATA
COMMENTS ON DESIREDATTRIBUTION
COMMENTS ON DESIREDGEOMETRY AND RESOLUTION
DRIVERS OFREFRESH CYCLE
Forest Land Classification
ODF and Counties
Informs fire response. Can impact taxassessments, must be verified and publically vetted, requires public meetings.
Characterizes lands as Ag,Grazing, or Timber. Need to distinguish “industrial forest land” from other types of forest land.
Requires some fieldwork toachieve reliable information at sub‐parcel level.
Ongoing basis, statewide by county. Takes 1‐2 years to get through a county.
Identification of Leaking Septic Systems
DEQ Transition‐zone urban/rural fringe an areaof particular interest, where a lot of rural residential use is concentrated but where urban services are not provided.
Size of property, age of system,year built.
Location of system onproperty if possible.
Policy Impact Assessments
All types of agencies
Identification of landowners who could beaffected or what other impacts might occur due to change in rules or new regulations, for example, stream side setbacks.
Fire Risk Assessment Fire Behavior Analysis
ODF Helps evaluate costs of potential lossesdue to wildfire, as well as costs of suppression efforts in different areas, depending on human use of the area as well as natural conditions.
Privately‐owned vs publicly‐owned, value of improvement and timber.
Public inquiries General Public Support for responding to queriespertaining to availability of land, for example, undeveloped residential land. These are inquiries made outside of a specific public process, e.g., a UGB expansion study.
County assessments of prop class and improvement value sometimes lag 1 or 2 years before being updated to reflect new development.
Current Planning
Land Use Code Enforcement
Cities
Counties
Spatial Surrogate Development
DEQ Provides basis for allocating county‐wideair emissions to sub‐county levels.
Zoning, roadway and roadwaysurface, vegetation, building footprint, crop type, fallow fields, pipelines.
As high‐resolution aspossible.
Forest plots DEQ Species composition NW Forest Plan