Using GIS for Collaborative Land Use Compatibility Planning Near Airports (2019)

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38 To ensure proper data integrity for addressing land use compatibility challenges with geo­ spatial technologies, technical specifications (including the extent, accuracy, frequency of update, level of attribution, and sensitivity) are recommended for each type of data. Accordingly, it is important to examine available data sources and data collection and maintenance procedures. Planning information systems are major sources of planning intelligence. They not only help stakeholders learn and understand the effects of changes in the urban area, but they also help them with land use challenges in a constructive manner, supported by sufficient data analysis. Data can be manipulated for a variety of political and market­oriented reasons. It is the planner’s responsibility to ensure that information systems are designed and used to benefit the commu­ nity while supporting the planning function. As in other planning responsibilities, this requires careful attention to fairness and efficiency. Geospatial technologies have the potential to restructure and enlarge the scope of plan­ ning analysis, enabling greater opportunity for collaboration between land use stakeholders. However, these systems can be complex undertakings. During the design and implementation of geospatial systems and subsequent data development, the overall purpose should be kept in mind. Therefore, what follows are data development and symbology recommendations. These can be applied at a base level for data development needs that are new or existing across airport departments and/or local governments. With respect to leveraging GIS technologies (once data development has occurred) for land use compatibility planning and governance, the details within the chapter focus on providing feature lists for the most critical issues as identified by the survey respondents: aircraft noise and obstructions to navigable airspace. This chapter also includes recommended feature types for less critical but yet still relevant issues: solar glare and glint and fauna that is hazardous to aircraft operations. Principles of Data Development When organizations seek to develop data and leverage geospatial technologies to enhance collaboration, there are several principles that should be considered. Because the size and com­ plexity of local governments or airport governance constructs vary widely, the availability of existing data will also vary. For those communities seeking to create new or adjust existing planning and geospatial tech­ nologies as well as to develop existing and capture new data, this guidebook recommends several fundamental principles to follow. C H A P T E R 5 Data Development Guidelines

Data Development Guidelines 39 Creating Data Standards The quality of data, including its consistency and comparability, is enhanced when data stan­ dards are available to support the collection and use of a data set. The development of data standards is not something done at the end of the data development process. It is very much a part of the data development process. Data developers for airports and communities should develop operational procedures to ensure that data standards become a key component of the data development process and to ensure their ongoing relevance and maintenance. There is a cost associated with creating data standards, but the cost of not creating data stan­ dards is likely to be even higher. This includes the loss of information due to staff changes, data redundancy, data conflicts, liability, misapplications, and decisions based upon poorly docu­ mented data. These costs should be factored into any organization’s data development budget. Incorporate Federal, State, Local, and Professional Standards When data is being developed, it is important to ensure that the specifications of the data are consistent with federal, state, and local data standards. This avoids duplication of effort and the development of conflicting data standards. Datasets should be based on a single set of agreed defi­ nitions and data standards, such as FAA’s AGIS standards or nationally accepted data standards from professional organizations, so data developers can draw from existing agreed definitions— ensuring a high degree of consistency and reducing data development time and cost. Some data standards have been developed by the Federal Geographic Data Committee (FGDC) to structure GIS data for land use compatibility planning. For example, Part 1 of the Geographic Information Framework Data Standard (which examines cadastral data) provides a data standard for parcels (Federal Geographic Data Committee 2018). The American Plan­ ning Association (APA) has also developed the Land Based Classification System (LBCS) (APA 2001) to extend and refine traditional land use classifications into more detailed categories and subcategories. (Although these standards provide a great deal of detail that can be useful for airports and local agencies, as well as their collaborative planning needs, it has not been widely adopted.) To ensure synergy with FAA AGIS data standards, an FAA representative participated in the original development of these standards to ensure that unique airport needs would be met. Local needs occasionally require more specificity that federal data standards provide. Where this is the case, it is possible to specify more granular data standards as long as they can be trans­ lated to federal or state standards. Clarify Purpose of Data Collection Before deciding on what data to collect and develop, it is important to be clear about the purpose of the data collection including the important policy or planning questions, or service provision needs for which data collection is required, and how having the data will help deliver more efficient and effective services. It can be tempting to decide on what data to include in the collection before the purpose of the data collection is fully defined. Define the Objectives of the Data Collection Once the purpose of the data collection has been defined, the data required to meet the objectives of the collection can be identified and developed. Data developed and included in the data collection need to meet the collection’s objectives as the cost of developing, collecting, compiling, validating and reporting data can be expensive.

40 Using GIS for Collaborative Land Use Compatibility Planning Near Airports Identify and (Regularly) Use Available Data Sources The availability of existing data sources should be explored and used where possible. An important principle of data development is that data needed to support secondary (or down­ stream) information purposes (such as reporting, policy, governance or decision support) should be derivable from primary data (point­of­service delivery data). Otherwise, data needed for downstream requirements would have to be developed and collected separately, resulting in significant additional costs because of the need to establish parallel data collection systems to support existing and new data flows, whose products may not necessarily be fully compatible. Similarly, data developed and collected for mainly statistical purposes should be used to provide feedback to improve and enhance primary service delivery. Acknowledge the Limitations of Data While it is important to be mindful of the opportunity to reuse existing data, it is also impor­ tant to be aware of any limitations of the data and to ensure these are acknowledged. For exam­ ple, aircraft noise measures can be sensitive to variation to policies and procedures that have been adjusted over time to mitigate noise complaints or to measure noise contours. Thus, comparisons over time may be problematic or might not necessarily be applicable to planners seeking to ensure land use compatibility. Data Development May be Incremental Data development should support incremental development, such that the scope of the data­ set is expanded over time. It may not be possible to develop all data required for a data set at the same time. Some data may be more readily agreed upon and easily collected, while other data may be more problematic and require more time to develop. Depending on the timelines and resources available, it may be better to stage the data development such that the scope of the data collection is expanded over time. Data Development Is System Independent Data development should ensure that data in the data set is well defined and standardized so that it can be compared independent of the organization, system, or tool that captures the data. Data development should not be limited by the capability of any particular system. This better enables cross­collaboration between airports and communities. Include Privacy and Security Policies Data development processes should take into account security policies and privacy issues, including ensuring compliance with the information privacy principles. Datasets should avoid the inclusion of data that may be regarded as private or confidential in nature. Airport data, particularly data related to air traffic control or utilities, might be considered too sensitive from a security perspective to be made available to the public. Similarly, parcel ownership and privacy guidelines should be carefully examined before displaying publicly. Accordingly, federal, state, and local privacy and security policies should be carefully scrutinized. Data Redundancy, Relevancy, and Utility Good data development should ensure compatibility of data collection and reporting require­ ments to avoid situations where the same data has to be collected, counted, or reported differ­ ently for different programs or departments. This will reduce the reporting burden on service providers and help to reduce costs. Careful attention and coordination should be considered across different organizations to ensure multiple data collection efforts for different projects do

Data Development Guidelines 41 not collect the same information, which could produce inconsistencies, depending upon timing, accuracy, and metadata granularity. Data Development Should Reflect (not Drive) Practice Data should be able to be collected as a by­product of service delivery or administrative prac­ tice. The data need to be relevant and meaningful to those collecting the data and be of benefit to all relevant stakeholders. Data development should take into account the business needs, feasibility of data collection, and appropriateness of the data. Where possible, data development should be based on data stakeholders already want or need to collect to garner planning and development efficiencies. Most importantly, data development should ensure that data collec­ tors are not constrained or forced to operate in ways outside usual practice. The Data Development Process Data development is a methodological process, based on an understanding of the information to be derived from practically using the data. It includes modeling data needs and clarifying the relationships between data. Key data concepts are identified and standardized using data ele­ ments. Data development results in the production of a set of data standards to ensure consistent collection and use of the dataset. Business Process (Context) and Needs Assessment It is important to be clear about the purpose of data development efforts and the benefits. Otherwise the effectiveness of the resulting data set may be greatly reduced. It is therefore neces­ sary to obtain an understanding of the business context within which the information is needed, and the high priority policy issues, questions and/or service needs before proceeding with data development. Needs analysis results in documentation of the business requirements for data (or a problem statement), the target population (e.g., community planners, economic developers, FAA flight procedures specialists, etc.) and governance environment, a description of the problem, gaps between expected and ideal outcomes, and the relative priorities of the business requirements. This helps to guide further data development, including identification of what data is required. While the need for data development may relate to a specific problem or purpose, the expected benefits at all levels (for example, policy, program development, and return on investment levels) should be acknowledged and clearly stated. Feasibility Developing a data set can be costly. Therefore, it is important to consider conducting a fea­ sibility analysis (even if it is rudimentary) to provide an indication of the scope of the data development and the resources required to support the project. Feasibility analysis includes the identification of stakeholders and their relevant level of interest, the data needed to sup­ port information requirements, analysis of what data are currently collected, and if sufficient resources are available to proceed with the data development project. Identification of Stakeholders In addition to understanding the business processes, for the data development process to be successful, it must reflect the shared interests of those who use or rely on the data. The feasibility study should identify relevant stakeholders, their information requirements, their availability to participate in data development activities, and their level of commitment to data development,

42 Using GIS for Collaborative Land Use Compatibility Planning Near Airports data use, and data maintenance. If only one stakeholder sees a return on the investment, the data development process runs the risk of losing consensus and limiting the potential for enhanced collaboration. Identification of Existing Data It is important to identify existing data sources and assess their currency and usefulness for the current purpose. It is not always possible to get information about every system used for collection purposes, but obtaining a representative sample is recommended. The feasibility study should identify information gaps and data that are/might be problematic in terms of obtaining accurate information. Where possible, data should be existing byproducts of the organization, that is, stakeholders already want or need to collect the data. When developing data for secondary uses (in relation to answering key policy questions) it may be necessary to undertake a cost–benefit analysis to deter­ mine whether it is cost­effective to collect data not directly required to meet stakeholder needs. As part of the feasibility study, it would be useful to assess whether the data currently being collected allow for comparison over time and if there are any existing standards, performance indicators or benchmarks. Identifying Data for Development An important part of data development is the detailed analysis of the data necessary to support business requirements, which in turn depends on the purpose of the data development, the sorts of questions that need to be answered, or problem that needs to be addressed. Core concepts are identified, and these are defined and standardized. As some of the required data may be less readily available or difficult to collect consistently, and there is a cost and effort involved in data collection, it may be necessary to identify a core (or minimum) set of data for collection, based on how the data can be collected in practical terms and the priority of the data. Future expansion of the data set to include data that requires a longer time to develop would set up a work program for future data development work. Data Model Requirements A data model is a useful tool for identifying and depicting data for development. The model provides a diagrammatic representation of the building blocks of data necessary to meet user information requirements. The model is used to provide the framework and context for the data identified and described. It is employed to help users identify and articulate data requirements and specify the business rules and relationships that exist between data. It is important that the data model is agreed to and signed off by stakeholders before proceeding with data develop­ ment. Otherwise, the data development team may find that they have to deal with changing data requirements throughout the data development process. Symbology For purposes of additional data integrity, it is important to examine symbology. In the geo­ spatial world, symbology is defined as the shapes and colors chosen to represent features on a map, and contributes almost as much to the communication of information as the data. GIS technologies are designed to utilize a basemap with features that provide orientation in a two­ dimensional, flattened view, similar to a paper map. Layers of additional data are displayed on top of each other in user­determined order. This stacking paradigm, though one of the great powers of a GIS for visualization, can lead to symbology conflicts among the various layers.

Data Development Guidelines 43 Symbols can obscure one another, detract from visual discrimination between layers where colors or shapes are too similar, command too much attention where colors are too bold, or display poorly against any basemap features present. Application: Aircraft Noise Data Sources Significant amounts of data and complex technical analysis are required to determine the areas of noise impact and operation to determine preventative and remedial measures to implement. To allow effective data collection, it is important to keep in mind what deter­ mines the following factors: • Amount of noise exposure determined by: – Aircraft types – Number of average annual day operations – Stage length – Nighttime weighting • Noise exposure distribution determined by: – Flight track locations – Flight track use – Runway configuration and use. Figure 9 depicts all types of data collected to utilize FAA’s AEDT Model to analyze noise exposure. The classification of the data in such groups as shown in the figure will facilitate the collection and processing of these data for all parties involved in the process. Potential sources for aircraft noise data include, but are not limited to: • Airport master plans: Airport master plans (and affiliated ALPs) are one of the preferred sources for airport activity forecasts and noise contours—particularly since information from these documents contains intended roles and future physical characteristics that extend at least 20 years into the future for the airport. These plans are tremendously useful for effective land use compatibility planning. Note: Presented at Part 150 TC Meeting #2, August 2017. Figure 9. AEDT data input.

44 Using GIS for Collaborative Land Use Compatibility Planning Near Airports • Airport noise monitoring and management systems: Airport noise monitoring is generally used to evaluate noise­abatement programs and to develop aircraft departure and arrival procedures that minimize the impact of aircraft noise based upon altitude, flight path, and time of day. Data from these systems can be downloaded in a variety of formats, and reports and can also be linked geospatially with flight radar tracking so that specific aircraft can be identified when noise limits are exceeded, operational requirements can be enforced, and new plans and policies can be developed for continuous improvement. • FAR Part 150 studies: Most air carrier and busier general aviation airports are required to conduct Part 150 noise compatibility studies. These studies contain existing and 5­year pro­ jected noise contours. At airports where noise impacts are expected to decrease in the future, the NEMs generated from Part 150 studies are used for land use compatibility planning purposes. If noise exposure is expected to expand beyond 5 years, then the noise contours will not provide sufficient long­term data. • Noise elements of community general plans: The noise contours depicted in general plans are similar to the noise contours from airport master plans, since they both represent adopted local policies. It is very important that planners make sure these contours are up to date due to their usefulness in compatibility planning. Usually, noise contours included in general plans are copies of ones from the most recent airport master plan. • Environmental documents: Environmental analysis prepared for major airport developments normally contain newly prepared noise contours with a 20­year projection, which sometimes make these data more recent than one included in the airport master plan. Application: Aircraft Noise Data Analysis Collecting aircraft noise and operations data is a primary prerequisite to mitigating these negative impacts on communities. Aircraft noise data are collected and analyzed in a variety of ways; field surveys that collect noise decibel readings from various physical points, at various times, is the primary method. The ultimate output of noise decibel readings is noise contour maps that show the spatial distribution of aircraft noise levels around an airport. A supporting element to noise decibel readings is aircraft flight track data. These data sources capture the historical geolocation of airport operations—including approach and departures— into a geospatial polyline format. Visualizing and analyzing the spatial distribution of approaches and departures provides a measured benefit in understanding communities most vulnerable and likely to experience excessive aircraft noise. In some cases, additional sources of aircraft noise may also need to be considered. These include: • Ground operations: In most cases, aircraft ground operations are not considered a significant source of noise. This type of noise (from engine run­ups) can be included in the calculation from the AEDT, however. If such activity is included as a noise factor in the AEDT calcula­ tions, reference to this fact should be noted in the description of the contours. • Helicopters: Because helicopters have distinct noise characteristics and follow different flight tracks than airplanes, their noise can be particularly noticeable. The inclusion of helicopter noise in computation at airports with moderate or high levels of helicopter activity is desirable. There are methods of obtaining and utilizing flight track data, ranging from sophisticated approaches that integrate third­party software (e.g., Harris) to processes that utilize data from solely governmental sources. The FAA’s Performance Data Analysis and Reporting System (PDARS) is one source of flight operations data that extracts and reports data directly from the FAA’s National Airspace System (NAS). The system consists of a dedicated network of computers located at FAA sites, using specialized software for collecting detailed air traffic management system data (Figure 10).

Data Development Guidelines 45 PDARS includes: • 20 domestic Air Route Traffic Control Centers (ARTCC’s) • 28 Terminal Radar Approach Control (TRACON) facilities • 27 ASDE­X equipped airports to compile data. As part of NAS, the data contained in the PDARS system is intended for government use only. The PDARS data differs from other NAS data in that it is enhanced to provide quality­ controlled flight track data. However, FAA compiles and distributes this data at no cost. Flight tracks include temporal attributes (timestamps) that enable individual flight operations to be linked to individual unique noise events. Data is delivered in a compressed Comma Separated Value (CSV) format, consisting of multi­ points for each aircraft track (within a relative distance of a particular airport). Each point has an index, flight ID, aircraft type, arrival airport, departure airport, latitude, longitude, altitude, and timestamp. After receiving the compressed CSV data, an Extract Transform and Load (ETL) process is implemented to convert data to a usable format. Application: Land Use Data in Relation to Aircraft Noise In analyzing the impact of aircraft noise on proximate communities and investigating appro­ priate mitigations, it is critical to determine the location of compatible and incompatible land uses in surrounding communities. Land use data and planning processes are a necessary comple­ ment to aircraft noise data and analysis. Land use analysis typically requires utilization of data Source: FAA System Operations Services (2015a). Figure 10. PDARS business rules, data origination.

46 Using GIS for Collaborative Land Use Compatibility Planning Near Airports from a variety of sources. Parcel­level land use classification data can derive from municipali­ ties, counties, and regional governmental agencies (e.g., MPOs). Further, affected areas around airports can span across multiple jurisdictions. In cases where varying land use data is utilized by cities and counties, a system of standards should be implemented to blend disparate land use categories. Other data elements that aid in land use planning are jurisdictional and regulatory elements like municipalities, parcels, zoning, and airport boundary. Many of these features can be obtained from respective county GIS departments, or derived by airport staff. In addition, Figure 11 outlines various physical land cover types and specific land uses. Com­ piling these data sources can aid airport staff in measuring and visualizing the spatial allocation of compatible and incompatible land uses/land covers around airport property. Land covers with more intense and frequent human use are generally less compatible with airport operations (e.g., residential land conflicts most with aircraft operation and noise). Conversely, some land covers/uses can better coexist with aircraft operations and more easily absorb subsequent nega­ tive externalities (e.g., agriculture, recreation, water bodies and industrial land). Noise compatibility of land use is determined by comparing aircraft’s day­night average sound level (DNL) values at the site to the values in the land use compatibility guidelines. Local land use jurisdictions may have noise and land use compatibility standards that differ from the FAA’s land use compatibility guidelines with respect to DNL 65 decibel (dB) in 14 CFR part 150. Although the FAA does not use local standards to determine the significance of noise impacts, these standards must be disclosed to the extent required under 40 CFR 1502.16(c) and 1506.2(d). Noise impact area is the region within the airport’s 65 DNL contour that is composed of incompatible land uses. These incompatible land uses are: • Residences of all types; • Public and private schools; • Hospitals and convalescent homes; and • Churches, synagogues, temples, and other places of worship. However, these uses are not deemed incompatible if certain mitigation actions have been taken, such as: • Airport acquisition of an aviation easement for aircraft noise; and • Acoustical insulation to ensure that the interior DNL due to aircraft noise is 45 dB or less. Aircraft noise and subsequent contour maps can be geospatially linked to parcel­level land use classification data from municipalities to indicate affected areas in relation to specific parcels of land and their land use Application: Aircraft Noise GIS Feature Type/Layer List Tables 6 through 9 display information on aircraft noise. Figure 11. Land use types and compatibility.

Data Development Guidelines 47 Demographic Information Feature Type GeoType Source Description CensusBlock Polygon U.S. Census TIGER/Line Shapefiles and TIGER Geodatabases These shapefiles or geodatabases bring together geography from the 2015 TIGER/Line Shapefiles and data from the 2011–2015 American Community Survey (ACS) 5-year estimates. CensusBlockGroup Polygon U.S. Census TIGER/Line Shapefiles and TIGER Geodatabases These geodatabases bring together geography from the 2015 TIGER/Line Shapefiles and data from the 2011– 2015 American Community Survey (ACS) 5-year estimates. CensusTract Polygon U.S. Census TIGER/Line Shapefiles and TIGER Geodatabases These geodatabases bring together geography from the 2015 TIGER/Line Shapefiles and data from the 2011– 2015 American Community Survey (ACS) 5-year estimates. Table 6. Aircraft noise: GIS features and layers—demographics. Land Use Planning Feature Type GeoType Source Description LandCover Polygon National Land Cover Database (2011) RASTER: Most recent national land cover product developed by the Multi- Resolution Land Characteristics (MRLC) Consortium. LandUseExisting Polygon County/City GIS, MPOs, Open Data Consortiums (i.e., MNGEO) The current land use classification, typically defined at the parcel level. LandUseProposed Polygon County/City GIS, MPOs, Open Data Consortia (i.e., MNGEO) The proposed land use classification identified during community planning activities and sometimes defined at the parcel level, but typically proposed land use units are larger than the parcel. AirportBoundary Polygon Airport A polygon, or a set of polygons, that encompasses all property owned or controlled by the airport for aviation purposes. AirportParcel Polygon Airport A tract of land within the airport boundary acquired from surplus property, federal funds, local funds, etc. Include easement interests in areas outside the fee property line as an airport parcel. County Polygon County/City GIS, U.S. Census Boundary line of the land and water under the right, power, or authority of the county government. Parcel Polygon County/City GIS, County Tax Assessors A single cadastral unit, which is the spatial extent of the past, present, and future rights and interests in real property and the geographic framework to support the description of the spatial extent. State Polygon U.S. Census Boundary line of the land and water under the right, power, or authority of the state government. Zoning Polygon County/City GIS A parcel of land zoned specifically for real estate and land management purposes; more specifically for commercial, residential, or industrial use. Table 7. Aircraft noise: GIS features and layers—land use planning.

48 Using GIS for Collaborative Land Use Compatibility Planning Near Airports Aircraft Noise Monitoring Feature Type GeoType Source Description NoiseMonitoringStation Point Airport Derived Location of equipment for long-term measuring of ambient noise levels. NoiseIncident Point Derived A formal complaint by an individual or group regarding excessive noise resulting from airport operations. Point indicates location where excessive noise occurred (preferred) or address of complainant. FlightProcedure Polygon Airport Derived A series of predetermined maneuvers with specified protection from obstacles. FlightTrackLine Polyline FAA NAS A line indicating the general flight track used in the vicinity of airfields. FlightTrackPoint Point FAA NAS A point in space that designates aircraft arrival and departure routes. NoiseContourCurrent Polygon Airport/Consultant Derived An area(s) that describes the noise level attributed to operations under current conditions of the natural and built environment. For aircraft operations, the Day/Night average sound level (Ldn) descriptor is typically used to categorize noise levels. NoiseContourFuture Polygon Airport/Consultant Derived An area(s) that describes the noise level attributed to operations under future conditions of the natural and built environment. For aircraft operations, the Day/Night average sound level (Ldn) descriptor is typically used to categorize noise levels. Table 8. Aircraft noise: GIS features and layers—noise monitoring. Application: Aircraft Noise Symbology One primary feature that visualizes aircraft noise and its impact on proximate areas is the noise contour polyline. While there is no standard symbology for this feature, commonly noise decibel levels are displays with polylines representing increments of 5 dB (see Figure 12). However, when data is available and pertinent, more specific and extensive noise dB levels can also be included and visualized, leveraging GIS tools such as AEDT. The key element in display­ ing noise contour features is the distribution of the 65­dB, 70­dB, and 75­db noise levels, as these noise measurements directly relate to accepted externalities to local land uses per FAR Part 150 Airport Noise Compatibility Planning Program. Corresponding flight track lines do not have an established, standardized symbology. How­ ever, there are effective practices within the industry that provide excellent examples of how to display aircraft noise data and effectively utilize this data to mitigate noise impacts. Minneapolis­ St. Paul International Airport (MSP) provides bountiful examples of displaying flight tracks and noise data. MSP’s Metropolitan Airport Commission (MAC) Noise Program Office has operated a comprehensive digital aircraft noise and flight track data collection and processing system since 1992. The MAC Noise and Operations Monitoring System (MACNOMS) provides tools to help MAC staff analyze noise impacts and visualize flight tracks in immediate airspace. MACNOM’s flight tracker tool (see Figure 13) enables the user to interactively view and replay flight activity—with 20 minutes of delay—that took place within 40 nautical miles of MSP. The tool visualizes arrivals and departures.

Data Development Guidelines 49 Land Use Compatibility Feature Type GeoType Source Description RecreationAreas Polygon Derived, City/County/MPO GIS The published representation of parks and recreation areas managed in the facility site point feature type, combined with service information and organized for consumption in desktop and web applications. ParkingLot Polygon Derived, City/County/MPO GIS An area of an airport used for parking of automobiles, buses, etc. RoadCenterline Polyline FHWA & City/County/MPO GIS Road segments representing centerlines of all roadways or carriageways in a local government. Typically, this information is compiled from orthoimagery or other aerial photography sources. This representation of the road centerlines supports address geocoding and mapping. It also serves as a source for public works and other agencies that are responsible for the active management of the road network. Waterbodies Polygon USGS / USDA Polygon features representing waterbodies. CommercialBusiness Point Derived, City/County/MPO GIS The published representation of commercial business and corporate headquarters managed in the facility site point feature type, combined with service information and organized for consumption in desktop and web applications. EducationFacility Point Derived, City/County/MPO GIS The published representation of educational facilities (schools, colleges, etc.) managed in the facility site point feature type, combined with service information and organized for consumption in desktop and web applications. HouseWorship Point Derived, City/County/MPO GIS The published representation of houses of worship managed in the facility site point feature type, combined with service information and organized for consumption in desktop and web applications. MedicalFacility Point Derived, City/County/MPO GIS The published representation of hospitals and medical facilities managed in the facility site point feature type, combined with service information and organized for consumption in desktop and web applications. OutdoorSportsFacility Point Derived, City/County/MPO GIS The published representation of outdoor sports facilities managed in the facility site point feature type, combined with service information and organized for consumption in desktop and web applications. Table 9. Aircraft noise: GIS features and layers—land use compatibility (Misc).

50 Using GIS for Collaborative Land Use Compatibility Planning Near Airports Noise Contours, 5-dB increments (55dB to 85dB) Noise Contours, 5-dB increments (55dB to 75dB) Figure 12. Geospatial-based noise contours at MSP. Figure 13. Geospatial-based flight tracks at MSP.

Data Development Guidelines 51 Figure 14 displays NEMs for MSP, FLL, and Philadelphia International Airport (PHL) with samples of different types of formats depicting the same type of information. However, pertinent land use plans and a general overview of existing and planned uses of the land should be described. The description of current noise conditions includes: • DNL contours or noise grid points showing existing aircraft noise levels. Noise exposure contours must include DNL 65­, 70­, and 75­dB levels (additional contours may be provided on a case­by­case basis). Noise grids are sized to cover the study area for noise analysis. Figure 14. Geospatial-based aircraft noise maps for land use compatibility.

52 Using GIS for Collaborative Land Use Compatibility Planning Near Airports Multiple grids may be created, but at least one grid consists of population centroids from the U.S. Census blocks. The differences in noise analysis for proposed airport development and other actions in the immediate vicinity of an airport and for air traffic airspace and procedure actions in a larger study area are described more fully in this guidance under the environmental consequences section. U.S. Census data may be supplemented by higher resolution data at the local municipality level, when available. Parcel­level data may be available from the local property appraiser’s office and is often updated at least once a year. Population and household information can be estimated at the parcel level provided that the local municipalities maintain current estimates of people per household and a housing unit count for multi­family parcels. • Number of residences or people residing within each noise contour where aircraft noise expo­ sure is at or above DNL 65 dB; or for a larger scale air traffic airspace and procedure action, the population within areas exposed at or above DNL 65 dB, at or above DNL 60 but less than DNL 65 dB, and at or above DNL 45 dB but less than DNL 60 dB. • Location and number of noise­sensitive uses in addition to residences (e.g., schools, hospitals, parks, recreation areas) that could be significantly impacted by noise; and • Maps and other means to depict land uses within the noise study area. The addition of flight tracks may be helpful. Illustrations should be sufficiently large and clear to be readily understood. The description of current noise conditions is usually confined to aircraft noise. However, the inclusion of other noise data, such as background or ambient noise or notable levels of noise in the study area from other sources (e.g., highways, industrial uses) is appropriate where such noise data is pertinent to understanding the affected environment and to considering the environmental impacts of the proposed action and alternative(s). Application: Obstructions to Navigable Airspace Data Sources Airport authorities, government agencies, and consultants have been analyzing obstructions for decades and have created an extensive system for storing airport­related information in spa­ tially aware computer formats aimed at use in a GIS. FAA created standards for aeronautical ground surveys and obstruction analysis: standard­ ized GIS data schema with data capture rules and CAD standard guidelines, and data collection requirements for various purposes (e.g., ALP and master plan creation and updates). FAA AGIS guidance organizes its schema at the highest level by categorizing data layers into groups of related layers, such as airfield, airspace, and geodetic (Figure 15). In the airspace Figure 15. FAA AGIS geodatabase schema: airspace features.

Data Development Guidelines 53 group are located the obstacle, obstruction area, and OIS layers, the primary layers for storage of data directly related to airspace obstructions. Once an organization has acquired obstruction data, it can leverage a GIS system to compare obstructions with land use and related data spatially to explore their relationships. For instance, where an identified obstacle is located (within the cadastral fabric surrounding), it may indicate the owner or controller of the obstructing object and offer a clue as to approach mitigation of the obstruction. Only the availability of land use and related data layers limits the discoverable relationships. Application: Obstructions to Navigable Airspace Data Analysis FAA AGIS standards dictate each layer is associated with a set of attributes that address the various purposes for the data, namely planimetric airport layout, master planning, approach procedure design and validation, and asset management. Regarding airspace obstructions, the obstacle layer, for instance, has attributes such as obstacle type, penetration value, and distance from runway end. Obstacle types and penetrations determine regulatory consequences, while relative distance attributes play a key role in procedure design. FAA developed a template database that implements the AGIS schema and represents an excellent starting point for an airport­related land use database (see Figure 15). The FAA tem­ plate has, for example, feature datasets Airfield, Airspace, Geodetic, and NAVAIDs, each con­ taining one or more feature types that are critical to collaborative obstruction analysis. At the heart of obstruction analysis lie two data entities that define the process: vertically projecting objects and physical standards that, in spatial comparison with the objects, identify objects that could be obstructions to flying aircraft. The spatial comparison usually consists of photogrammetric (triangulation in stereo aerial photography) measurement of object location and top elevation, which requires specialized, expensive hardware and software and time­intensive expert operation. The source stereo aerial imagery, too, represents a weighty cost. Consequently, many local governments or other organizations located around airports and participating in collaborative land use cannot practically supply the obstructions data for the effort. Federal regulations require airports to identify obstructions to airspace on a regular basis (and also help pay for it), so airport authorities are the logical source of these high­quality obstructions data. FAA makes the Digital Obstacle File (DOF) available for download, describing all the obstacles reported to FAA that are deemed of most interest to U.S. aviation. The physical standards, most often referred to as obstruction identification (OID) surfaces or obstacle surfaces are 3D, planar, imaginary objects, defined in federal regulation, and located in near­airport airspace, through which objects may or may not penetrate. Whether the surfaces are drawn in a CAD system, generated automatically by a computer algorithm, or otherwise constructed, a GIS system natively handles such geometric entities. As the photogrammetric identification of obstruction candidates necessarily utilizes the OID surfaces, requiring their creation early in the process, airport authorities again constitute the practical source of these surfaces. The next step in obstructions analysis, calculation of objects’ vertical penetrations of surfaces, lends itself consummately to a GIS­based analysis, and local organizations with GIS capability, in addition to airport authorities, can utilize these surfaces and the obstruction objects data in such an analysis.

54 Using GIS for Collaborative Land Use Compatibility Planning Near Airports Generally, local governments or planning organizations commonly have already implemented GIS systems and have a greater capability to consume the data than airport staff, who often have multiple responsibilities apart from GIS analysis. Furthermore, many possibilities exist for con­ version of these data to a multitude of electronic formats, whatever an agency may need. A range of GIS systems exists, enjoying worldwide use, with each system handling its own list of data formats, but the richness of formats and systems available should allow any collaborative land use planning venture to implement an excellent GIS. Airports often have developed or will soon be developing AGIS data, and an airport sponsor owns the AGIS data it has developed. Airports are free to share their AGIS data with their surrounding communities. Of note, parameters for constructing the core OID surface sets defined in AGIS and Part 77 regulations rely almost solely upon runway end coordinates, electronic approach guidance types available for a runway, and a few physical and operational runway characteristics. Readily available, public sources for these data are AirNav.com, AirportIQ 5010, FAA approach proce­ dure plates, and FAA web datasheets. Another helpful source is the Aviation Weather website. One OID surface parameter critical to Part 77 surface construction is whether a runway is classified as a utility runway. AvnWx.com— unlike AirNav.com, AirportIQ 5010, and others presenting information from the FAA 5010 airports information database—presents this value in its airport details listing. In the interactive map on the website, locate the airport of interest, click on it, and then click on the “Details” link in the upper right corner of the popup dialogue. In the listing for each runway, the item labeled “Objects Affecting Navigable Airspace (CFR Part 77),” will indicate the runway type, including whether the runway is a utility runway. This item often reveals other critically important runway parameters, too, like approach type and visibility minimums, though these data are available elsewhere. Although not critical for obstruction analysis, some additional feature types could pro­ vide information to clarify or augment analyses, to identify gaps in the obstacle data, or to address planned development on or around the airport. Some of these feature types (e.g., FlightTrackLine and NoiseContour) would especially support investigation of the interplay of obstructions evaluation and land use planning. One would have to acquire these data, though, through a request to the FAA or to third parties, like consultants. For land use purposes, the geodatabase (GDB) template has the Cadastral feature dataset, which contains applicable feature types, such as land use, airport parcel, parcel, state [bound­ ary], county [boundary], municipality [boundary], lease zone, and zoning. As the main purpose of AGIS standards is not land use planning, the attributes for each of these feature types— and possibly the selection of feature types available—may well be inadequate for collaborative land use planning around airports; however, database managers can easily add feature types and attributes to the schema where needed. If available, these data collections will most likely reside with local planning and public works departments, state departments of transportation, or state GIS data repositories. Finally, raster (image) datasets at the least add powerful visual information for orientation and base mapping and at a critical level comprise the basis of photogrammetric identification of obstacles through stereo aerial imagery, the primary means for obstacle identification. The latter stereo imagery datasets, typically covering the airport and the surrounding area out to the extent of applicable OIS, usually prove, though, to be extremely large (multiple GDBs), so storage for acceptable performance usually demands local storage or distribution from a closely connected (and fast) server. Mosaiced and compressed orthoimagery derived from the stereo frames would make the eas­ iest to use and best performing format for base mapping. Satellite imagery can also contribute

Data Development Guidelines 55 useful information to the collaborative land use planning process where land cover datasets have been prepared. Though not the same concept as land use, land cover does typically exhibit clas­ sification schemes considerably like those of land use, so land cover can act as a proxy for or approximation of land use. Typically, obstruction maps will layer obstacles, represented as points, over layers to be exam­ ined in comparison with them, in this case land use and related layers. Typically, obstacle points are colored according to some scale of surface penetration amount. A quick, visual overview of such a map, carefully symbolized, can pinpoint obstructions of particular import. Application: Land Use Data Related to Obstructions to Navigable Airspace The obstructions issue interacts with the number one­ranked issue, that of aircraft noise, a relationship that may call for further modification of the data schema to address more thor­ oughly aspects of aircraft noise, and discussed in detail throughout the guidebook. (AGIS stan­ dards schema does have NoiseContour, NoiseIncident, and NoiseMonitoringPoint feature types in its Environmental feature dataset.) Obstacles figure prominently in approach procedure design, and procedures in turn greatly determine where aircraft fly. Aircraft flight paths, then, represent potential “noise corridors,” especially at the lower approach altitudes closer to the airport. The presence of irritating or even unhealthful noise may affect the appropriateness of various land uses under heavily used flight paths. One reality of obstructions to airspace that further emphasizes the obstructions­land use relationship is that many obstacles are located outside of airport property and are owned or controlled by local entities. Regulations require airport authorities to analyze obstructions and to deal with obstructing objects in specific ways. Local entities, however, may have concerns, especially where the obstructions are prominent and valued trees or stands of trees, valuable buildings, or unusually tall structures, like cell towers or cranes. Collaboration and cooperation would serve well to resolve such conflicts. Collaborative land use planning that routinely investigates the airspace obstructing effect of proposed structures and features of the natural landscape, like trees and their growth over time, would boost the effectiveness of collaborative efforts by precluding conflicts in the first place. Accordingly, Part 77 provides an avenue to propose a new object to the FAA and to evaluate that object against a set of OIS defined in that regulation. Penetration by a proposed object would identify that object as an obstruction and would signal the FAA to evaluate the object and to determine if that object is an actual hazard to aircraft navigation. Application: Obstructions to Navigable Airspace GIS Feature Type/Layer List Table 10 to 12 display information on GIS features and layouts. Application: Obstructions to Navigable Airspace Symbology All collaborative land use planning and obstructions analysis efforts will involve multiple airport and non­airport stakeholders, who need to be able to communicate with each other to come to a mutual understanding of the land use and airspace obstructions conditions at hand

56 Using GIS for Collaborative Land Use Compatibility Planning Near Airports and in the future. As with most spatially based investigations, thoughtfully designed maps of data, analyses, and results constitute a clear, intuitive, and highly informative medium of com­ munication, capable of revealing and clarifying obscure relationships among data. GIS systems excel at such visualization. In the context of airspace obstructions and land use, a GIS needs to display—at a minimum— obstacles as points, OIS as polygons, and land use areas as polygons, all with a symbology that does not conflict. A further consideration would be to choose symbology already established as a de facto or formal standard in the industry to leverage its familiarity to enhance communication. For exam­ ple, the industry commonly uses the hat­and­dot symbol, , to represent obstacles. The FAA, for instance, uses this symbol in its published approach procedure plates (FAA 2017a). Also, one might consider mimicking symbology presented in the relevant regulatory documents. In FAA guidance RunwayProtectArea Polygon Derivation from definitions as contained in federal regulations and guidance Obstruction surfaces representing specialized runway protection areas, e.g., Approach Light Plane. AirportControlPoint Point Ground survey or derivation, depending on type of point Points relevant to geodetic ground survey and imagery control and to airport layout and physical structure. NavaidEquipment Point Ground survey Locations of various electronic and visual aids to aircraft navigation; for obstacle analysis critical for ground locations of first and last lights/light bars in an Approach Lighting System used in constructing certain obstruction surfaces. Obstruction Analysis Feature Type GeoType Source Description RunwayEndPoint Point Field Survey measurement Critical point at each runway end that serves as the starting point for critical surfaces with respect to obstruction analysis. RunwayHelipadDesignSurface Polygon Derivation from definitions as contained in regulations and guidance, primarily -13 Various surfaces involved in the design of an airport, mostly safety-related. Some of these surfaces are defined as obstacle standards. FlightProcedure Line Derived from published locations of waypoints/flyover points and approach procedure plates Primarily for storage of attribute information required for deriving obstruction surfaces and analyzing obstacles; also useful for visualization of obstruction-related data. Obstacle Point Photogrammetric measurement or field survey Vertically projecting objects that may interfere with the safe movement of flying and taxiing aircraft. ObstructionArea Polygon Photogrammetric measurement Area of regulatory imaginary surface where a group of like objects, like trees or buildings, penetrate the surface. ObstructionIdSurface Polygon Derivation from definitions as contained in federal regulations and Imaginary, 3D surface against which to compare Obstacle points for various regulatory purposes. Table 10. Obstructions: GIS features and layers—obstruction analysis.

Data Development Guidelines 57 AGIS standards, Airport Airspace Analysis guidance contains oblique, 3D, colored diagrams of OID surfaces, the colors of which could be reflected in a GIS map of OID surfaces. In practice, the symbology for OID surfaces has proven critical for map clarity. OID surfaces typically overlap, and when their polygon symbols utilize opaque color fills, those polygons obscure each other and other features, including the basemap. Using polygon outline symbology with no color fill and using color fill transparency are the two common solutions to this prob­ lem. Their efficacies vary, though, with the number of surfaces displayed and with the basemap chosen. Additionally, various GIS systems will handle transparency differently. Hence, thorough symbology testing is highly recommended, regardless of the underlying GIS platform. The significance of an obstacle lies in its penetration of a particular surface and presents challenges for obstruction visualization. Because surfaces usually overlap, often extensively, obstacles frequently occur within the boundaries of multiple surfaces. Clear association of an obstacle with the surface it most penetrates is an important goal for obstacle visualization. Additionally, because regulations usually define OID surfaces in sets, each associated with a dif­ ferent runway, one useful display technique is to draw the surfaces for each runway separately, superimposing the obstacle points penetrating just those surfaces. Furthermore, this technique then isolates and emphasizes the obstacle points, providing an opportunity to convey more information through appropriate choice of obstacle symbology. Being a continuous value, obstacle penetration is perhaps best presented in ranges, each Obstruction Analysis – Additional Supporting Features Feature Type GeoType Source Description AirfieldLight Point Planimetric measurement or ground survey Any lighting located within or near an airport boundary that provides guidance for airborne and ground maneuvering of aircraft. Stopway Polygon Planimetric measurement or ground survey An area beyond the takeoff runway able to support an aircraft during an aborted takeoff. FlightTrackLine Line FAA, 3rd Party Vendors Recorded flight tracks of aircraft during approach and departure phases of flight. FlightTrackPoint Point FAA, 3rd Party Vendors Points of significance along recorded flight tracks of aircraft during approach and departure phases of flight. WaypointsFlyovers Point AirNav.com GPS waypoints and flyover points for approaching and departing aircraft. AirQualityArea Polygon Consultants An area indicating an average level of air pollution. NoiseContour Polygon Consultants An area that describes the noise attributed to operations. For aircraft operations, the Day/Night average sound level (Ldn) descriptor is typically used to categorize noise levels. NoiseIncident Point Public Location of a formal complaint by an individual or group regarding excessive noise resulting from airport operations. Building Polygon Planimetric measurement; MPO databases Man-made structures for occupancy, industry, or storage; modeled with a bounding polygon. Tower Point Planimetric measurement; plans filed with local municipalities Man-made structure intended to facilitate an activity at an elevated position above the ground; should include meteorological test towers erected in advance of project development. Table 11. Obstructions: GIS features and layouts—additional supporting features.

58 Using GIS for Collaborative Land Use Compatibility Planning Near Airports represented by the same shape but a distinct color. For example, 0–3 feet penetration could be gray, 3–10 feet orange, and > 10 feet red. Choice of symbology will greatly determine the useful­ ness of a map. Application: Fauna that is Hazardous to Aircraft Operations Table 13 displays feature classes that are required to use GIS to analyze and mitigate the hazards that birds and other wildlife can pose to aircraft operations. Application: Interference: Solar Glare and Glint GIS Feature Type/Layer List Table 14 displays feature classes that are required to use GIS to analyze the glare and glint impact solar panels can have on pilots when operating aircraft on the ground or in the air, as well as the impact on air traffic controllers. Features for Land Use Planning Feature Type GeoType Source Description AirportParcel Polygon Airport planning A fee-simple tract of land within the Airport Boundary or an airport-purchased easement area outside of the Airport Boundary. County Polygon Regional MPOs; state/regional GIS data repositories Boundary line of the land and water under the right, power, or authority of the county government. LandUse Polygon Regional MPOs; state/regional GIS data repositories A description of the human use of land and water. LeaseZone Polygon Airport planning A parcel of land that is leased by airport authorities for airport-related uses. Municipality Polygon Local government; regional MPOs; state/regional GIS data repositories Boundary line of the land and water under the right, power, or authority of municipal government. Parcel Polygon Local/municipal government; regional MPOs; state/regional GIS data repositories A single cadastral unit, which is the spatial extent of an area of real property. State Polygon Municipal/federal government; regional MPOs; state/regional GIS data repositories Boundary line of the land and water under the right, power, or authority of the state government. Zoning Polygon Municipal government; regional MPOs A parcel of land defined specifically for real estate and land management purposes, more specifically for commercial, residential, or industrial uses. CensusBlock Polygon Derived from U.S. Census Bureau TIGER/Line files Boundary of a contiguous group of census tracts. CensusBlockGroup Polygon Derived from U.S. Census Bureau TIGER/Line files Boundary of a contiguous group of census blocks. CensusTract Polygon Derived from U.S. Census Bureau TIGER/Line files Boundary of smallest census unit defined. Table 12. Obstructions: GIS features and layers—land use planning.

Fauna that is Hazardous to Aircraft Operations - Analysis Feature Type GeoType Source Description AirportBoundary Polygon ALP Exhibit A; Airport Cadastral Records; Local Parcel Maps A polygon, or a set of polygons, that encompasses all property owned or controlled by the airport for aviation purposes. FaunaObservation Point The National Oceanic and Atmospheric Administration (NOAA) offers an on-line Solar Position Calculator. The location, date/time, species, and number of wildlife observed. FaunaFlightTrack Line Radar installed at the airport. The track of one or more birds recorded by radar (if installed). FlightProcedure Line Derived from published locations of The tracks arriving and departing aircraft are to follow when arriving to waypoints/flyover points and approach procedure plates and departing from the airport. FlightTrack Line FAA or third part radar or multilateration systems. The recorded tracks taken by aircraft that have arrived to or departed from the airport. LandUseExisting Polygon County/City GIS, MPO, Open Data Consortiums (i.e., MNGEO) The current land use classification, typically defined at the parcel level. LandCover Polygon National Land Cover Database (2011) A classification indicating vegetation, soil, water, and other land cover that can provide an attractive habitat for hazardous wildlife. FaunaHabitatArea Polygon Field observation An area of land where fauna have been observed to gathered or live. CoordinateGridCell Polygon Airport developed A regularly sized group of grid cells that can be used to generalize wildlife observations or describe habitat areas. Table 13. Fauna that is hazardous to aircraft operations—analysis. Interference: Solar Glare and Glint - Analysis Feature Type GeoType Source Description SolarPanel Polygon Design or As-Built Data of solar panel installation projects The extent of the structure of a contiguous array of solar panels. SunPosition Point NOAA offers an on-line Solar Position Calculator. Record the azimuth and angle above the horizon at specific locations and dates/times. FlightProcedure Line Derived from published locations of waypoints/flyover points and approach procedure plates The tracks arriving and departing aircraft are to follow when arriving to and departing from the airport. Runway Polygon Photogrammetry or Ground Survey The extent of runway movement surfaces at an airport. TaxiwayElement Polygon Photogrammetry or Ground Survey The extent of taxiway movement surfaces at an airport. Apron Polygon Photogrammetry or Ground Survey The extent of apron movement and parking surfaces at an airport. AtctCab Polygon Design or As-built drawings; May be derived from Building or Tower data so long as the horizontal and vertical of the ATCT cab floor is captured. The location of the Air Traffic Control Tower Cab Floor. This is the floor on which air traffic controllers sit or stand when controlling aircraft in the air or on the ground. Table 14. Interference: solar glare and glint—analysis.

60 Using GIS for Collaborative Land Use Compatibility Planning Near Airports Table 15 displays feature classes that are required to analyze the interference solar panels can have on airport navigational aids and communications equipment. The feature classes required to analyze the impact of solar panels as obstructions to navigable airspace are the same as those listed in the obstructions portion of this chapter. Interference: NAVAIDS and Communications - Analysis Feature Type GeoType Source Description SolarPanel Polygon Design or As-Built Data of solar panel installation projects The extent of the structure of a contiguous array of solar panels. NavaidEquipment Point Ground survey Locations of various electronic and visual aids to aircraft navigation; for obstacle analysis critical for ground locations of first and last lights/light bars in an Approach Lighting System used in constructing certain obstruction surfaces. Tower Point Photogrammetry or Ground Survey The location of critical communication transmitting or receiving towers not included as NAVAIDS. Table 15. Interference: NAVAIDS and communications—analysis.