Methods for GIS-Driven Airspace Management: Integrating Unmanned Aircraft Systems (UASs), Advanced Air Mobility (AAM), and Crewed Aircraft in the NAS

Highlights

What are the main findings?

• A Geographic Information System (GIS) framework successfully integrated diverse aviation data (Automatic Dependent Surveillance–Broadcast (ADS-B), Unmanned Aircraft System (UAS) flight logs, Federal Aviation Administration (FAA) data) into unified 2D and 3D models for the visualization and quantitative analysis of shared airspace operations.
• A case study was utilized to apply the framework to analyze a critical UAS and crewed aircraft interaction near Purdue University Airport (KLAF), which confirmed the ability of GIS models to precisely measure spatial separation, finding a minimum separation of approximately 459 feet laterally and 339 feet vertically.

What are the implications of the main findings?

• GIS offered a scalable, centralized platform to address historical Air Traffic Management (ATM) and Unmanned Aircraft System (UAS) Traffic Management (UTM) integration deficiencies, significantly improving shared airspace safety oversight and situational awareness.
• The findings established that combining the GIS framework with Artificial Intelligence (AI) and advanced sensors was critical for modernizing the National Airspace System (NAS), supporting the future development of real-time risk prediction and dynamic airspace management.

Abstract

The rapid growth of Unmanned Aircraft Systems (UASs) and Advanced Air Mobility (AAM) presents significant integration and safety challenges for the National Airspace System (NAS), often relying on disconnected Air Traffic Management (ATM) and Unmanned Aircraft System Traffic Management (UTM) practices that contribute to airspace incidents. This study evaluates Geographic Information Systems (GISs) as a unified, data-driven framework to enhance shared airspace safety and efficiency. A comprehensive, multi-phase methodology was developed using GIS (specifically Esri ArcGIS Pro) to integrate heterogeneous aviation data, including FAA aeronautical data, Automatic Dependent Surveillance–Broadcast (ADS-B) for crewed aircraft, and UAS Flight Records, necessitating detailed spatial–temporal data preprocessing for harmonization. The effectiveness of this GIS-based approach was demonstrated through a case study analyzing a critical interaction between a University UAS (Da-Jiang Innovations (DJI) M300) and a crewed Piper PA-28-181 near Purdue University Airport (KLAF). The resulting two-dimensional (2D) and three-dimensional (3D) models successfully enabled the visualization, quantitative measurement, and analysis of aircraft trajectories, confirming a minimum separation of approximately 459 feet laterally and 339 feet vertically. The findings confirm that a GIS offers a centralized, scalable platform for collating, analyzing, modeling, and visualizing air traffic operations, directly addressing ATM/UTM integration deficiencies. This GIS framework, especially when combined with advancements in sensor technologies and Artificial Intelligence (AI) for anomaly detection, is critical for modernizing NAS oversight, improving situational awareness, and establishing a foundation for real-time risk prediction and dynamic airspace management.

1. Introduction

The rapid proliferation of Unmanned Aircraft Systems (UASs), commonly referred to as drones, along with the advancement of Advanced Air Mobility (AAM) technologies, are reshaping the structure and operations of modern aviation. This expansion, driven by technological miniaturization, has made UASs more agile, affordable, accessible, and adaptable, while also enabling a greater degree of anonymity. As a result, new challenges have emerged for the National Airspace System (NAS), requiring innovative approaches to ensure safe and efficient airspace management [1].

As of July 2025, the Federal Aviation Administration (FAA) reported a total of 822,039 registered UASs, including 377,484 recreational flyer registrations, 433,407 commercial UAS registrations, and 11,148 paper-based registrations for aircraft over 55 pounds, which require submission of AC Form 8050-1 [2]. Although the growth rate of recreational and hobbyist UAS registrations has begun to slow, overall ownership continues to rise. The FAA projects that the number of registered small UASs will reach approximately 1.97 million units by 2029 [3]. On the commercial side, small UAS operations are at a pivotal point for sustained expansion, with forecasts indicating the fleet exceeded one million units in 2025 and will grow to 1.118 million by 2029, a 22% increase over the totals recorded at the end of 2024 [3].

While UAS activity represents a significant component of this evolving landscape, the aviation sector is also adapting to the emergence of new aircraft categories through the certification of multiple AAM platforms. The Vision 100–Century of the Aviation Reauthorization Act of 2003 marked a pivotal modernization milestone, leading to the Next Generation Air Transportation System (NextGen) initiative. These efforts have sought to transform Air Traffic Management through the adoption of emerging technologies such as Trajectory-Based Operations (TBO), supporting an information-centric approach to managing national airspace [4].

Despite this long-term vision, the number of UAS-related incidents and near misses continues to rise. With thousands of crewed aircraft and UAS simultaneously operating in U.S. airspace, even a small number of UAS incursions can significantly increase collision risk, particularly near critical approach and departure corridors. A study conducted at Dallas-Fort Worth Airport (KDFW) by Embry–Riddle Aeronautical University and Unmanned Robotic Systems Analysis (URSA) identified 24 Near Mid-Air Collision (NMACs) between August 2018 and July 2021, where UASs came within 500 feet of crewed aircraft [5]. Although most operators follow established rules, including the 400-foot altitude limit, a small number of repeat offenders were responsible for more than half of the recorded incidents. Most of these encounters occurred within 1.5 miles of runway approach or departure zones, areas that are particularly sensitive to airspace intrusions [5]. These findings cast doubt on the effectiveness of tools such as the Low-Altitude Authorization and Notification Capability (LAANC), introduced in 2016 with the goal of reducing unauthorized UAS activity by 30 percent. To date, this performance benchmark has not been conclusively validated through follow-up research [6]. Limited evaluations, such as Wallace et al.’s 30-day data collection study at Daytona Beach International Airport (KDAB) and follow-up cases at KDFW, provide insight but do not establish comprehensive trends [5,7]. These gaps in empirical evidence highlight the need for detailed trajectory-based studies to quantify interactions between UASs and crewed aircraft to evaluate the effectiveness of current Air Traffic Management (ATM) and Unmanned Aircraft System (UAS) Traffic Management (UTM) measures.

UAS-related safety risks are not limited to domestic airspace. In July 2023, a Boeing 787 departed from London Heathrow and climbed through 3000 feet when the flight crew observed a UAS pass within approximately 20 feet vertically and 20 m horizontally. This event, along with a similar incident involving an Airbus 320 on final approach to the same airport in August 2023, was classified by the National Air Traffic Services (NATS) as Risk Category A, defined as situations in which a serious risk of collision exists [8].

Together, these incidents illustrate a fundamental problem. While broad planning has been underway to facilitate the integration of crewed and uncrewed aircraft, current mitigation practices still rely on outdated airspace strategies. These include visual separation protocols, static classification structures, and disconnected systems for situational awareness across both ATM and UTM domains. Such methods no longer provide sufficient control in environments characterized by dynamic interactions among UASs, AAM platforms, and conventional aircraft.

This situation is further compounded by rising levels of airspace congestion, limited visibility into real-time operations, and ineffective coordination between uncrewed and crewed systems. For example, the collision between a civilian UAS and a Canadair CL-415 “Super Scooper” firefighting aircraft during the January 2025 Palisades Fire, and a similar event involving a UAS and a rescue helicopter during the July 2025 Central Texas floods, demonstrate the real-world consequences of these shortcomings within emergency response and disaster relief operations [9,10].

The small UAS (sUAS) Traffic Analysis (A11L.UAS.91) Final Report (March 2025) offers a critical, data-driven foundation for informing future UAS policy and operational oversight. The study establishes a structured framework to collect and analyze empirical data on UAS activity within low-altitude airspace, an essential step given the persistent absence of a centralized authority for comprehensive UAS data aggregation and analysis [11]. Key findings highlight systemic gaps: the absence of robust, nationwide UAS tracking mechanisms; limitations in Remote ID range and reliability; continued growth in commercial UAS operations; and frequent exceedances of altitude restrictions by operators. The report also underscores notable discrepancies between LAANC authorizations and actual flight behavior, the absence of LAANC-based protections for crewed aircraft, heightened risks surrounding aerodromes and heliports, and the need for refined altitude guidelines to support the integration of Urban Air Mobility (UAM) and overall AAM operations [11]. Collectively, these insights reveal a fragmented operational environment that hinders situational awareness and underscores the urgent need for a unified, data-centric framework capable of supporting safe and scalable UAS integration within the NAS. The specific knowledge gap addressed here is the lack of a validated, spatiotemporal methodology to unify these disconnected ATM and UTM data streams for precise 3D conflict analysis, representing a significant theoretical hurdle in modernizing airspace oversight.

As UAS and AAM operations expand, the demand for a more integrated and responsive Traffic Management infrastructure continues to grow and collaboration between organizations like Environmental Systems Research Institute, Inc. (Esri), ArcGIS Velocity, and FlightAware are exploring real-time solutions [12]. Traditional ATM systems, which were designed for predictable, crewed aircraft behavior, are now strained by the growing complexity and density of operations in shared airspace. What is urgently needed is a unified framework that can dynamically manage both crewed and uncrewed aircraft with the help of real-time data, intelligent automation, and interoperable sensing systems.

This research explores how Geographic Information Systems (GISs) may serve as a practical and scalable solution to these challenges. A GIS provides a centralized environment for managing, visualizing, and analyzing data related to air traffic operations. By integrating sources such as Automatic Dependent Surveillance–Broadcast (ADS-B), Remote ID signals, Radio Frequency tracking (e.g., Da-Jiang Innovations (DJI) AeroScope, Shenzhen, China), and FAA aeronautical data, GIS enables users to identify operational patterns, visualize potential conflicts, and implement proactive safety measures in shared airspace.

To address the need for data-driven methods that can strengthen both ATM and UTM environments, this study utilized the objectives in application to a case study, referred to as the “DJI M300 RGB and PDU57 Interaction Case Study”. This case study is specifically used to demonstrate and validate the methodology by focusing on the spatial and temporal interaction between a specific UAS (DJI Matrice 300 RTK) and a crewed aircraft (PDU57). This provides a tangible scenario against which the system developed from the objectives can be evaluated for its ability to identify potential operational conflicts and assess airspace utilization.

The expected academic contribution of this research is the establishment of a validated, multi-phase GIS framework that transitions airspace safety from reactive incident reporting to proactive, quantitative analysis. By achieving the following objectives, this work provides the technical foundation necessary for integrating Artificial Intelligence (AI) and real-time sensors to predict and mitigate risks in shared airspace.

Objective 1:

Identify and assess relevant aviation datasets, including airspace information, crewed aircraft trajectories, and UAS activity, for integration into a GIS, and determine the preprocessing steps required for effective use such as data cleaning, transformation, and format conversion.

Case study application: This objective will involve identifying and preprocessing the specific flight logs (trajectory, telemetry, and operational data) from the DJI M300 RGB and the PDU57 aircraft. This includes cleaning and transforming their raw data formats (e.g., .CSV) into a consistent, geospatial format suitable for GIS analysis.

Objective 2:

Develop and implement GIS-based methods to model, visualize, and analyze interactions between UAS and crewed aircraft within the NAS, with an emphasis on spatial and temporal representations that support assessments of operational conflicts and airspace utilization.

Case study application: The developed GIS methods will be applied to the preprocessed DJI M300 and PDU57 trajectories. This involves the following:

• Modeling: Creating 2D maps, 3D scenes, and animation models of concurrent flight paths.
• Visualization: Generating spatial representations showing the proximity and separation distances between the two aircraft.
• Analysis: Using spatiotemporal analysis techniques to quantify the risk of operational conflict by identifying instances where the projected separation distance falls below defined safety margins, and assessing the efficiency of the airspace used during the interaction.

2. Materials and Methods

The following procedures focus on a multi-phase analytical approach designed to resolve the inherent complexities of integrating diverse aviation data. These methods were selected for their ability to harmonize disparate coordinate systems, varying altitude baselines, and inconsistent temporal formats into a single, synchronized platform. This level of technical detail is provided to ensure full replicability of the workflow, a necessity for establishing a transparent and dependable framework for shared airspace oversight.

The methods utilized in this study were created in a multi-phase workflow process developed over a 10-month period using quantitative and qualitative methods for exploratory research, which included literature reviews, consultations with academic and industry professionals, and systematic processes for data collection, management, and analysis. The workflow was designed to support the application of GIS techniques to air traffic and unmanned Traffic Management scenarios.

To provide a direct demonstration of the developed GIS methodologies, a real-world airspace incursion event, the “DJI M300 RGB and PDU57 Interaction Case” was explored. This incident involved a near miss or Near Mid-Air Collision (NMAC), with a proximity of less than 500 feet between a university-operated UAS and a crewed aircraft, which served as the primary scenario for applying spatial and temporal analysis techniques to quantify separation distances and traffic dynamics with GIS [13].

In 2023, a Purdue University DJI M300 was conducting an RGB (Red, Green, Blue) mapping mission near the Purdue water tower on the Northwest side of campus. Just after 5:00 PM Eastern, the DJI M300 UAS operator was alerted of an aircraft entering their mapping mission airspace and took action to increase separation until the threat had passed. The DJI M300 UAS team had appropriate LAANC approval and waivers in place to conduct their mission and had a VHF radio monitoring KLAF Tower communications. The DJI M300 UAS team observer was able to take the photo in Figure 1 noting the aircraft as a Piper Archer with Purdue University paint scheme, although the side number was difficult to make out due to image quality. This subsequent post-flight research analysis confirmed the aircraft to be a Purdue University Piper PA-28-181 Archer TX, callsign “PDU57”, as identified through an ADS-B receiver and provides a detailed narrative and timeline of events within the analysis to follow.

2.1. Study Area

The study area airspace shown in Figure 2 is centered between the one-hour operational track of the DJI M300 UAS (provided for reference in dark green) and Purdue University Airport (KLAF). The DJI M30 UAS was operating on a waiver above the intersection of four LAANC boxes (noted between 0, 100, and 200 feet). Although the UAS operation was geographically limited, the study area boundaries, shown in the Locator, are significantly broader. This expanded scope was necessary because the ADS-B data for the PDU57 crewed aircraft originating from KLAF extended across multiple sorties within a 24 h period, covering the Northwest and North Central regions of Indiana.

2.2. Objective 1: Aviation Data Sources and GIS Integration Considerations

This research utilizes a diverse and heterogeneous set of aviation data sources to support integrated GIS analysis of crewed aircraft and UAS operations within the NAS. The methodology begins by addressing the need for harmonization across distinct data categories: airspace boundaries (sourced from FAA Aeronautical Information Services, including Class Airspace and LAANC data), dynamic air traffic trajectories (including Automatic Dependent Surveillance–Broadcast (ADS-B) for crewed aircraft and internal Flight Records for UAS), and system-specific data formats. The comprehensive integration process, therefore, requires detailed, multi-step data preprocessing, specifically to resolve discrepancies in altitude referencing (e.g., barometric vs. height above takeoff location) and to standardize temporal formatting into the structure required for seamless spatiotemporal modeling and animation within the GIS environment.

2.2.1. Data Sources for Airspace

Setting up a GIS environment for data analysis requires understanding the different forms of geospatial data. Specific datasets, such as those from Esri’s Living Atlas of the World [14], are hosted online and accessed directly within a GIS platform, serving primarily as base maps or reference layers. Other data, such as aviation shapefiles or geographic datasets, are downloaded locally to allow the creation of custom feature layers and the ability to conduct offline analysis. Tabular data, often in the form of spreadsheets (.CSV or .XLSX), can also be imported and joined to spatial layers to enrich analysis with additional attributes. Additionally, geospatial data hosted on platforms like Esri’s ArcGIS Online [15] may not always be curated, and the quality can vary depending on the source.

Variations in GIS data quality across sources can significantly impact preprocessing requirements and analysis outcomes. Curated datasets from the Esri Living Atlas, which are accessed directly from Esri ArcGIS Pro 3.3.1, are generally clean and structured, making them immediately suitable for analysis. In contrast, data that must be saved locally and imported into ArcGIS Pro (such as downloaded Aviation Datasets or Shapefiles) or user-hosted layers added directly from ArcGIS Online may require preprocessing, cleaning, and validation to ensure consistency, correct formatting, and accurate spatial referencing. Similarly, tabular data (e.g., Excel or CSV files) must be imported and joined to spatial layers, often requiring formatting adjustments, removal of errors, and proper alignment. Recognizing these differences in data sources, access methods, and preparation is essential for accurate GIS analysis and is a key consideration when integrating multiple data sources into a single project.

Table 1. Types of GIS data and uses.

Types of GIS Data and Uses
Data Type	Source	Purpose/Use in GIS	Notes on Quality/Preparation	Coordinate System/Units
Hosted Layers	Esri Living Atlas	Visualization	Generally Curated and Clean	WGS 84/Degrees
		Base Map Reference		Projected Layers in Meters
Downloaded Spatial Data	Aviation Datasets	Feature Layer Creation	May Require Cleaning and Validation	WGS 84/Degrees
	Shapefiles	Offline Analysis		Feet or Meters Depending on Dataset
Shapefiles	Offline Analysis	Custom Feature Layer Creation	May Require Cleaning, Validation, and Attribute Alignment	WGS 84 or Local Projected Coordinate System/International Systems of Units (SI) or Imperial Units as a part of the U.S. National Institute of Standards and Technology (NIST)
		Offline Analysis
		Spatial Queries
Tabular Data	Excel	Attribute Enrichment	Often Require Formatting and Alignment	N/A (Inherits Projection from Spatial Join)
	CSV Files	Analysis
User-Hosted Layers	ArcGIS Online	Visualization	Quality Varies	WGS 84/Degrees
		Analysis Reference	May Require Cleaning

summarizes the distinct types of GIS data, their sources, and typical uses in a GIS workflow.

For all datasets summarized in Table 1,

Table 2. Airspace geospatial datasets from GIS base layers and FAA.

Airspace Geospatial Datasets
Source	Data	File Type	Name	Coordinate System/Units
U.S. Census Data—Topologically Integrated Geographic Encoding and Referencing (TIGER) Line Files	U.S. State Boundaries GIS Data	SHP	Continental US States	WGS 84/Degrees
Federal Aviation Administration (FAA) Aeronautical Information Services (AIS) Aeronautical Data Delivery Service (ADDS)	Aviation GIS Data		Airports	WGS 84/Degrees Or Feet or Meters Depending on Dataset
			Airspace Boundary
			Class Airspace
			National Defense Airspace TFR Areas
			Prohibited Areas
			Route Airspace
			Special Use Airspace
Federal Aviation Administration (FAA) Aeronautical Information Services (AIS) UAS Data Delivery Service (UDDS)	Aviation GIS Data (Specific to UAS)		FAA UAS Facility Map Data
			National Security UAS Flight Restrictions
			Prohibited Areas

and

Table 3. Airspace geospatial datasets for DJI M300 RGB and PDU57 interaction case study.

Airspace Geospatial Datasets for DJI M300 RGB and PDU57 Interaction Case Study
Source	Data	File Type	Name
U.S. Census Data—Topologically Integrated Geographic Encoding and Referencing (TIGER) Line Files	U.S. State Boundaries GIS Data	SHP	Continental US States → Indiana
Federal Aviation Administration (FAA) Aeronautical Information Services (AIS) Aeronautical Data Delivery Service (ADDS)	Aviation GIS Data		Airports → KLAF
			Class Airspace → KLAF Class D and KLAF Class E
Federal Aviation Administration (FAA) Aeronautical Information Services (AIS) UAS Data Delivery Service (UDDS)	Aviation GIS Data (Specific to UAS)		FAA UAS Facility Map Data → KLAF LAANC

, information about coordinate systems and units of measurement is included directly in the tables. Unprojected layers use the WGS 84 geographic coordinate system, with latitude and longitude expressed in degrees, while projected data sets employ appropriate local coordinate systems with units in meters or feet, depending on the dataset. Tabular or system-specific data, such as range/bearing outputs from detection systems, are noted in the tables and can be converted to WGS 84 for integration with other GIS layers.

All airspace data pertinent to this research, including Class Airspace, Special Use Airspace, and LAANC data, were generated from specific downloads provided by the Federal Aviation Administration (FAA) Aeronautical Information Services (AIS) ArcGIS information, Table 2 [16,17]. The data sourcing process was executed via two primary services. The Aeronautical Data Delivery Service (ADDS) was utilized to download shapefiles (.SHP) containing Airports, Airspace Boundary, Class Airspace, National Defense Airspace TFR Areas, Prohibited Areas, Route Airspace, and Special Use Airspace. Additionally, the UAS Data Delivery Service (UDDS) was employed to acquire FAA UAS Facility Map Data for LAANC information, Prohibited Areas, and National Security UAS Flight Restrictions shapefiles [18].

To set up the “DJI M300 RGB and PDU57 Interaction Case Study” research area within the GIS, the U.S. State Boundaries feature layer was downloaded from Esri’s Living Atlas of the World in addition to utilizing the included Light Gray Base layer [14,15]. To create the airspace feature layers, the FAA ADDS and UDDS data were downloaded; however, not all layers were necessary or utilized based on the geographic size of the research area [17,18]. Therefore, select features were exported into a distinct shapefile or geodatabase feature class as described in Table 3.

2.2.2. Data Sources for ATM/UTM

There are multiple data sources and technologies available for tracking both crewed aircraft and UAS. Data sources align to external detection systems or internal systems that self-report data to receivers. The types of systems available for tracking aircraft on the FAA’s Air Traffic Technology page include distinct types and ranges of radar, ADS-B, Trajectory-Based Operations (TBO) within Next Generation Air Transportation System (NextGen), multi-lateration sensors, and additional technologies under development [19]. The FAA’s Unmanned Aircraft System Traffic Management (UTM) page refers to the UTM Plan to expound upon the use of UAS networks for deconfliction which is related to Radio Frequency (RF) communication bands, 5G networks, satellite communication links, etc. [20,21]. The UTM Plan also explores the expanse of remote identification of UAS with Remote ID and the expansion of additional capabilities in a phased approach to Beyond Visual Line of Sight (BVLOS) and technologies under development [20,21].

Although numerous innovative detection and tracking systems for both crewed aircraft and UAS were explored (

Table 4. List of available ATM/UTM data sources.

List of Available ATM/UTM Data Sources
System Types	Applicable to	Typical File Type/Format	Source(s)/Examples	Coordinate System/Units
Acoustic Detection and Identification Systems	Crewed Aircraft Including AAM and UAS	Import .CSV Range/Bearing from Receiver		Local Cartesian (Meters) or System-Specific WGS 84/Degrees
Communication Networks	Crewed Aircraft Including AAM and UAS	Import .CSV Latitude/Longitude	5G, Satellite Communications (SATCOM), etc.	WGS 84/Degrees
Flight Plans and Reports	Crewed Aircraft Including AAM	Import .TXT Latitude/Longitude	Visual Flight Rule (VFR) Flight Plans	WGS 84/Degrees
			Instrument Flight Rule (IFR) Flight Plans
			Composite (VFR/IFR) Flight Plans
			International Flight Plans
			Defense Visual Flight Rules (DVFR) Flight Plans
	UAS		Low-Altitude Authorization and Notification Capability (LAANC) Authorizations
			Waivers
Radar Systems	Crewed Aircraft Including AAM and UAS	Import .CSV Latitude/Longitude	Air Traffic Control (ATC)	WGS 84/Degrees; Local Projected Meters for Radar Grid, If Needed
			Low-Altitude Detection and Identification Systems
RF Detection and Identification Systems	UAS	Import .CSV Range/Bearing from Receiver	Dedrone by Axon	Local Cartesian (Meters) or Convert to WGS 84 Can be Converted to WGS 84
			DJI AeroScope
Optical Detection and Identification	Crewed Aircraft Including AAM and UAS		Electro-Optical and Infrared (EO/IR)	System-Specific; Georeferenced to WGS 84, If Coordinates Available
Self-Reported Aircraft Data	Crewed Aircraft Including AAM	Import .CSV Latitude/Longitude	Automatic Dependent Surveillance–Broadcast (ADS-B)	WGS 84/Degrees
	UAS		Remote ID

), the final data selection for this study was dictated by accessibility, necessitating reliance on available sources and excluding unavailable technologies like FAA radar or acoustic, RF, and optical sensor network data.

The “DJI M300 RGB and PDU57 Interaction Case Study” GIS coordinate system projection was set to WGS 1984 Web Mercator (auxiliary sphere). The datasets for airspace, dump1090 ADS-B, and DJI M300 Flight Record were all in imperial standard units of measurement, therefore no additional conversions were required.

2.2.3. Altitude Conversions

The altitude for crewed aircraft or UAS position data is reported by detection systems or self-reported in various formats to include Above Ground Level (AGL), Mean Sea Level (MSL), or be described in Flight Level (FL). Crewed aircraft report altitude via ADS-B Out as part of a position report broadcast once per second [22]. The self-reported aircraft data will include barometric altitude (“baroaltitude”) based on the air pressure/setting in the altimeter and may include geometric altitude (“geoaltitude”) based on GNSS positioning calculations, if equipped. The barometric altitude reported will be based off the altimeter settings provided by ATC or a nearby airfield’s Automatic Terminal Information Service (ATIS), inherent errors due to instrumentation and weather factors should be recognized for comparison of aircraft based on systems utilized for reporting [23].

The separation analysis in this case study was conducted using GNSS-derived geometric altitudes from both aircraft to establish a high-precision, common vertical reference frame. For the Piper PA-28-181, positional accuracy was derived from the onboard GNSS receiver feeding the ADS-B Out system. This equipment is required to meet FAA performance standards under 14 CFR §91.227, which necessitates a Navigation Accuracy Category for Position (NACp) of less than 0.05 nautical miles (approximately 303.8 feet). This analysis assumes the aircraft utilized a Wide Area Augmentation System (WAAS)-enabled receiver, providing a horizontal positional accuracy of less than 2 m (approximately 6.6 feet) for approximately 95% of reported positions [24]. Crucially, the altitude used for this study was the geometric altitude (“geoaltitude”) reported via ADS-B, which is derived directly from GNSS measurements rather than barometric pressure.

While most UASs with altitude reporting features utilize barometric sensors to report height above the takeoff location, the DJI M300 used in this encounter operated with Real-Time Kinematic (RTK) positioning enabled. By leveraging an RTK base station or network, the aircraft’s positional accuracy was improved to ±0.1 m (0.3 feet) in both vertical and horizontal dimensions [25]. Flight log analysis confirms that the RTK system remained active with no alerts of degraded performance, and the recorded transitions between Waypoint and P-GPS modes were the result of the remote pilot manually assuming control to increase vertical separation rather than system anomalies.

Because the DJI M300 reports GNSS-derived Z-coordinates as ellipsoidal height referenced to the WGS-84 ellipsoid, these values may differ from Mean Sea Level (MSL) or barometric altitudes. However, due to the localized nature of the study area, any horizontal variation in geoid separation was negligible. Since both the Piper PA-28-181 (via ADS-B geoaltitude) and the DJI M300 (via RTK-GNSS) utilized geometric altitude references, the inherent uncertainty regarding vertical datum reconciliations, such as barometric-to-geometric conversions, was significantly reduced. This common reference frame provides a high degree of confidence in the calculated separation distances and ensures the measurements remain precise and unaffected by atmospheric reference offsets.

When detection systems, such as a DJI AeroScope, are utilized, the reported altitude data will be sourced directly from the UAS onboard sensors [26]. Detection systems such as DJI AeroScope require line of sight and may also be inhibited by the RF attenuation. In previous exploratory research, the SI (metric) was utilized as the standard unit of measurement and AerialArmor.com data from a DJI AeroScope had to be converted for UAS’ flight altitude data [26]. An additional “AltitudeMeters” field was added to the Attribute Table, and a conversion was made from feet to meters by “Calculate Field” (AltitudeMeters = Altitude × 0.3048).

The GIS user must set up altitude conversions for each available data source correctly to ensure that additional errors are not introduced into the spatial analysis outside of known system and environmental inaccuracies.

2.2.4. Time Conversions for Animation

Whether the crewed aircraft or UAS position data is reported by detection systems or self-reported, information will include position reports in the x-, y-, and z-axis by time. The crewed aircraft and UAS must be analyzed in a spatial context in terms of time as all aircraft move through three-dimensional space on their respective flight paths, thus transitioning through multiple airspaces and/or block altitudes during a single flight.

Visualizing data across time within a GIS environment, such as in spatial animations, requires the preparation of data into specific supported time formats [27]. To enable this functionality, a time conversion field in the proper format was necessary. For the required level of temporal detail, which extended down to the second for both crewed aircraft and UAS reporting, the utilized Esri ArcGIS Pro 3.3.1 GIS required a “YYYYMMDDhhmmss” format.

The effective temporal resolution after synchronization was 1 Hz. While the DJI M300 flight logs provided high-frequency telemetry (typically recorded at 10 Hz), the synchronization was aligned to 1 Hz to match the standard broadcast frequency of the crewed aircraft’s ADS-B Out system, which reports once per second. By matching the high-resolution UAS telemetry to the nearest common second of the ADS-B data, the framework enabled a precise 1:1 spatiotemporal comparison. This approach allowed for the quantification of the closest point of interaction without the need for interpolation or resampling, as the 1 Hz resolution provided sufficient granularity for the interaction window analyzed.

To ensure the final animation could be successfully executed in Esri ArcGIS Pro 3.3.1, a “TimeConversion” field was added to each crewed aircraft (dump1090 ADS-B) and UAS (DJI Flight Record) dataset. This field converted the disparate time formats into the single, required format “YYYYMMDDhhmmss,” as detailed in

Table 5. Excel time conversions for animation.

Excel Time Conversions for Animation
Create “TimeConversion” Field	ADS-B Data	dump1090-127_0_0_1 (Full Receiver Range)	Sum (date + time)
			Format to MM/DD/YY HH:MM
			Format to YYYYMMDDhhmmss
		data (Parser-Limited Geographic Area)	Format Field 6 Time HH:MM:SS
			Sum (Field 5 + Field 6)
			Format to YYYYMMDDhhmmss
	DJI M300 Flight Record	DJIFlightRecord_(16-28-15) DJIFlightRecord_(16-57-58)	Sum (date [local] + updateTime [local])
			Format to MM/DD/YY HH:MM
			Format to YYYYMMDDhhmmss

.

If the time data is reported in a non-standard unit, such as a Unix timestamp, or is in a text format, individuals with advanced programming knowledge can add an additional field to the dataset’s Attribute Table and use custom code in the “Calculate Field” tool to perform the conversion. Python, a widely used language for date and time manipulation, is especially effective for such tasks in both ArcGIS Pro and QGIS. Alternatively, time conversion can be managed externally by appending a new conversion field to the dataset after performing the necessary transformations and data cleaning in a Microsoft Excel spreadsheet. Once cleaned and manipulated, the Table can be uploaded to GIS for further analysis.

The integration of these diverse data sources necessitated careful preparation to ensure both accuracy and interoperability within the analysis environment. To preserve data integrity across disparate systems, consistency was established across foundational data elements. Specifically, consistent units of measure were applied; altitude and time formats were properly converted to a common standard, and consistent Coordinate Reference Systems (CRSs) were established across all spatial datasets. Once these foundational elements were aligned and datasets were thoroughly cleaned and standardized, the resulting aviation data became sufficiently dependable for deeper spatial analysis and meaningful interpretation.

2.3. Objective 2: GIS Methods for Modeling and Analyzing UAS Crewed Aircraft Interactions

The GIS-based framework utilized in this study is structured around a three-part methodological architecture designed to bridge ATM and UTM data gaps:

• Data ingestion and harmonization subsystem: Processes heterogeneous sources (ADS-B, UAS logs, FAA AIS) through format conversion (e.g., Excel to Table) and merging to resolve row-limit constraints.
• Spatiotemporal alignment subsystem: The core logical engine that executes Geometric Standardization (x, y), Vertical Normalization (z), and Temporal Synchronization (t) to unify disparate datums into a single 4D Coordinate Reference System.
• Visualization and analytical subsystem: Translates unified data into 2D maps and 3D scenes, enabling direct measurement of separation distances and ‘Pattern-of-Life’ analysis through time-series animation.

This architecture ensures that the relations between raw data, processing logic, and final visualization remain transparent and replicable.

2.3.1. Airspace

In flight planning software such as AirNav.com or ForeFlight, an Electronic Flight Bag (EFB) can provide the necessary details for creating airspace feature layers in a GIS [28,29]. Airspace does not always conform to the general descriptions and standard dimensions proposed in guidance, such as the Pilot’s Handbook of Aeronautical Knowledge [30]. The height or altitude of airspace may be referenced as AGL, MSL, or FL.

To create three-dimensional (3D) visualizations, each corresponding airspace polygon needs to be separated, as this allows each to be extruded within the z-axis for corresponding altitudes. The 3D visualizations such as airspace can be created and referenced in either SI or NIST as the feature layer referencing units can be set for display. “Extrusion under Linear Referencing,” as well as the “Elevation” within the layers’ “Properties”, both have this option and allow for one less step in conversions of multiple data source information. Each feature layer requires additional numeric fields for the base and maximum altitude of the airspace, if not already included in the dataset.

In this research, KLAF’s Class D and E Airspace, surrounding Special Use Airspace, and each block altitude section of the LAANC boxes were separated into individual layers. Each feature layer required additional fields to be created in the Attribute Table for the upper and lower limits of the airspace, which allows it to be extruded in the z-axis for 3D scenes. The base altitudes of feature layers were set in “Properties,” “Elevation”, and utilizing the “Base” field created to the correct vertical units. The “Feature Layer” tab within the “Extrusion” ribbon was utilized to create the max altitude of the airspace by selecting the “Absolute Height” of the created “Altitude” field and the correct units. The preceding steps were taken for each of the desired airspace layers to set up the area for analysis.

2.3.2. ATM/UTM

Crewed aircraft and UAS position report data were modeled and animated using the spatial and temporal visualization capabilities inherent to GIS. The analysis was conducted within both two-dimensional (2D) maps and three-dimensional (3D) scenes to facilitate comprehensive spatial and vertical (z-axis) height/altitude assessment.

The study utilized the comprehensive Geoprocessing Toolsets available within the GIS environment for the necessary data conversion and management required for subsequent modeling and analysis. The following section highlights the common geoprocessing workflow developed for the creation of the ATM/UTM model and animation. This workflow was established based on lessons learned during the exploratory research phase [31].

The Esri ArcGIS Pro 3.3.1 Conversion Toolbox contains tools that convert source data into usable formats [32]. For example, the “Excel” tool can be utilized to convert ADS-B, DJI AeroScope, DJI Flight Record, or other similar Microsoft Excel files to and from tables. The Export Features tool can be utilized to convert a feature class or layer to a new feature class, typically after isolating specific attributes within an Attribute Table or making selections on a map or scene.

Data can be manipulated through the Data Management Toolbox, or additional toolboxes pending the GIS software utilized [33]. It may be necessary to modify table data utilizing the Table Toolset or add a field for an altitude or time conversion as previously noted utilizing the Fields Toolset. As Microsoft Excel is limited to 1,048,576 rows of data, large position reporting files may require the GIS user to upload multiple .CSV files into a table, then utilize the Merge function in the General Toolset to combine all the information into one file. The Features Toolset contains the XY Table to Point tool which is utilized to convert the position report data within a Tables to Feature layer. If there is a preference to view flight tracks via point positions, then a Points to Line tool can create line features based on a Sort field such as ICAO24, callsign, or other unique identifier particular to that aircraft. To manipulate or isolate specific attributes within an Attribute Table, utilize Select by Attributes or, within geoprocessing, utilize the Select Layer By Attribute tool. This process allows for the selection of data based on attribute queries, which are useful in manipulating or creating new features such as a single airport airspace, flight track for a single aircraft, or position reports only within a specific altitude block.

The development of the analytical workflows utilized various Geoprocessing Toolsets to execute complex spatial operations. The initial workflow development was conducted using ArcGIS Pro 3.3.1. The functionality within these toolsets allows for the automated execution of multi-step analytical routines that would be impractical to perform manually. Such tools, commonly organized within standard GIS platforms, provide essential capabilities for spatial data processing and analysis [34,35].

To enable the generation of dynamic 3D scenes from position report data, additional processing steps were performed to prepare each feature layer for both elevation and time-based filtering. Static visualizations only permitted analysis of planar spatial relationships at a single point in time. In contrast, configuring feature layer properties to incorporate the z-axis (elevation–height/altitude) and time-series data facilitated the analysis of complex vertical separation and dynamic traffic conflicts. Feature layers representing stationary components (e.g., airspace boundaries and regional limits) were configured to remain constant throughout the animation, while layers containing moving assets (e.g., crewed aircraft and UAS) were filtered based on their respective time attribute values to accurately represent their movement.

The “Time” field must be in a supported time format, and the “Time Extent” should be calculated based on the time span for the data within the layer. The “Time Interval” can be configured as desired or set within the “Animation” or “Time” tabs while creating the animation [36]. Once all the feature layers are configured, an animation can be created within the “Animation” or “Time” tabs. The “Time” tab is useful for creating “Span” data for viewing, adjusting steps or intervals, and setting various parameters for analysis [36]. The “Animation” tab is useful for capturing the animation from various distinct “Key Frame” positions and/or for exporting to various media files. Pending the size of the export media file created, it may be difficult to share via a distributed application such as email. “Movie Export Presets” such as YouTube can create a compact MP4 file that can be uploaded via YouTube and shared via a “Private,” “Unlisted,” or “Public” link.

The example workflow in Figure 3 is a simplified version of the process necessary to take position report data and convert it to useable GIS flight tracks for analysis. This workflow is also representative of a ModelBuilder workflow within the Esri ArcGIS Pro 3.3.1 GIS [37]. The GIS data flow model’s visual programming language structure allows the user to create geoprocessing workflows, making it easier to duplicate repetitive tasks. The model can run tools in step-by-step sequence connecting data to tools for data management through analysis. Models can be utilized within other models or turned into geoprocessing tools that can be shared or utilized in Python scripting. For those with limited coding experience, the GIS data flow model allows for visualization of workflow in a sequential diagram [37].

To create the model and animation for the UAS and crewed aircraft, each of the respective position report datasets will be processed through a workflow, as shown in Figure 3. The Excel to Table geoprocessing tool was utilized to convert the .CSV file to a table. The dump1090 ADS-B data covered 24 h and contained 2,061,405 position reports, and due to Excel’s limit of 1,048,576 rows, data was broken into two .CSV files. Once both were uploaded as tables, they were merged with the Merge geoprocessing tool into one complete table.

The XY Table to Point tool was then utilized to convert the dump1090 ADS-B table into a feature layer with points for each position report. Although not used for this case study analysis, if there is a preference to view flight paths, then Points to Line would be used to create line flight tracks based off the ICAO24, callsign, or other unique identifier by aircraft.

To manipulate or isolate specific attributes within an Attribute Table, the Select by Attributes or Select Layer By Attribute tool within geoprocessing was utilized to perform attribute queries. Noting that the PDU57 ADS-B position reports were closest to the DJI M300 UAS, an attribute query as shown in Figure 3 was utilized to isolate those position reports for the time between 5:05:09 PM and 5:06:39 PM. The Export Features tool was then utilized with those position reports selected in the Attribute Table to convert the information to a new feature class for detailed analysis. Multiple attribute queries were performed to isolate only the PDU57 track from all other ADS-B aircraft tracks, the UAS controller (operator) position from the DJI M300 Flight Record, UAS points while in “Precision Global Positioning System (GPS), known as (PGPS)” mode from other mission points, etc. Although this case study did not require the crewed aircraft or UAS tracks to be isolated to a specific airspace, previous exploratory research has demonstrated the usefulness of spatial joins to determine how many aircraft operated within specific airspace boundaries or altitude blocks. These techniques also support pattern identification by analyzing tracks outside controlled airspace. This sequential workflow functions as a spatiotemporal alignment framework. By applying Geometric Standardization to (x, y) coordinates, Vertical Normalization to z-axis values, and Temporal Synchronization to t-intervals, the logic unifies heterogeneous data into a comparable 4D model. Conditional Filtering via attribute queries then serves as an analysis step to reduce data noise and extract only the features relevant to the specific interaction event.

Once the modeled UAS and crewed aircraft interaction were visualized within the 2D map and/or 3D scene environment, the data were subjected to spatial analysis using core GIS functionalities [38]. These functionalities included direct measurement capabilities, which were used to quantify distances, areas, angles, directions, and offsets between features and locations. When measuring the separation distance between aircraft, it was critical to account for the temporal component of the position reports. Measurement modes were configured to support geodesic, planar, loxodromic, or great elliptic calculations, alongside both imperial and metric units. These fundamental measurement capabilities provided a basis for manual analysis, which was complemented by more advanced processing routines executed via available GIS analysis tools.

In the absence of a unified ‘conflict’ standard for UAS-to-crewed aircraft encounters, this research adopts the FAA’s 500-foot Near Mid-Air Collision (NMAC) threshold as the quantitative definition of an operational conflict [13]. This distance-based metric follows established research precedents, such as the study at Dallas-Fort Worth Airport (KDFW), which utilized the 500-foot threshold to identify and quantify near-miss events between UAS and crewed aircraft [5]. While commercial Traffic Collision Avoidance Systems (TCAS) in manned aviation utilize dynamic time-based Traffic Advisories (TA) or Resolution Advisories (RA) based on the time-to-collision, the diverse performance characteristics of UAS make time-based metrics less uniform, The NMAC standard provides a static, objective distance benchmark suitable for post-flight GIS analysis and spatial separation measurement. This 500-foot threshold allowed for a consistent evaluation of the proximity between the DJI M300 and the PDU57 throughout the documented interaction.

An animation was created for the KLAF 3D scene within the “Animation” and “Time” tabs. The time was adjusted to start at 17:00:00 and end at 17:10:00 with a 10 s step interval. A series of 16 key frames were set at various positions to capture a 45 s animation of the interaction. The MP4 media file created was 48,535 KB and difficult to share via most methods. When exporting the movie, “Movie Export Presets” was utilized to create a compact MP4 file that was shared via a “Private” link, in this case “YouTube.”

2.3.3. Geoprocessing Tools

As noted, the initial research was conducted using Esri’s ArcGIS Pro 3.3.1, which included approximately 1000 geoprocessing tools, providing numerous options for analysis. With the release of subsequent versions through ArcGIS Pro 3.6, additional tools and enhanced functionalities are now available, and QGIS continues to develop with increased open-source resources, expanding potential analytical workflows.

Table 6. List of ArcGIS Pro Geoprocessing Tools for analysis [39,40,41,42].

List of ArcGIS Pro Geoprocessing Tools for Analysis
Toolbox	Analysis Use Description	Toolset Examples
Three-Dimensional Analyst Tools	Toolsets to analyze geometric relationships and feature properties	Three-Dimensional Proximity Toolset, Near 3D: Calculates 3D distance between feature and nearest feature
		Visibility Toolset, Geodesic Viewshed: Utilized to determine surface locations visible from observer features, such as line of sight for sensor receiver
Analysis Tools	Toolsets to perform fundamental proximity analysis and calculate statistics	Overlay Toolset, Erase: Creates a feature class by removing a portion of the content. Useful for the creation of layered airspace such as Class B or Class C and allows for analysis of aircraft specifically operating within those layers
		Overlay Toolset, Spatial Join: Joins one feature to another based on spatial relationships. Useful when associating multiple aircraft to specific airspace for analysis
		Proximity Toolset, Near Table: Calculates distances and proximity information into a stand-alone table for multiple near features
		Proximity Toolset, Near: Calculates distances and proximity information for closest feature class or layer
		Statistics Toolset, Summary Statistics: Calculates summary statistics for fields in a table, such as max, min, mean, etc. Useful for determining statistical information in position reports including altitudes, speed, time, etc.
Aviation Tools	Toolsets for analysis of aviation content	Airports Toolset, Analyze Airport Features: Calculates information such as point feature distance from runway centerline or end of the nearest runway
		Airports Toolset, Analyze Runway Obstacles: Determines if obstacles are penetrating the Obstruction Identification Surfaces (OIS) for a runway
Spatial Statistics Tools	Statistical toolsets for analysis of spatial distributions, patterns, processes, and relationships	Mapping Clusters Toolset, Hot Spot Analysis or Optimized Hot Spot Analysis: Identifies statistically significant hot spots and cold spots, potential to identify preferred routing for aircraft based on established and routine flight paths or identify areas of increased UAS activity
		Utilities Toolset, Collect Events: Converts incidents to weighted point data, useful for aviation incident reporting such as UAS violating airfield airspace

presents a “List of ArcGIS Pro Geoprocessing Tools for Analysis” based on methods implemented for the study of UAS and crewed aircraft interactions. While not exhaustive, the table highlights key toolboxes for exploration and provides examples of toolset applications. The toolboxes covered include 3D Analyst Tools, Analysis Tools, Aviation Tools, and Spatial Statistics Tools [39,40,41,42].

The Aviation Toolbox lacked extensive toolsets to compare aircraft to airspace, specific block altitudes, or other aircraft. Overall, the toolbox did not contain sufficient tools outside of analysis for data management, conversion, or additional functions. There remains a potential for additional tools to be created specifically for aviation data that would fill gaps and limitations within Esri’s ArcGIS Pro Geoprocessing Tools. Previously noted methods and workarounds were implemented to manage and manipulate aviation data such as 2D airspace feature layer extrusion for 3D scenes.

This research employs 2D and 3D mapping and modeling to accurately represent the vertical and horizontal movements of crewed aircraft and UAS within the NAS, providing detailed spatial context for analyzing interactions in complex airspace environments. These visualizations are complemented by pattern-of-life analysis, which identifies recurring flight paths, temporal trends, and potential conflict hotspots, offering valuable insight into operational behaviors and safety considerations. Together, these methods establish the analytical framework, while the case study demonstrates the effectiveness of these GIS-based approaches as presented in Section 3, Results.

3. Results

3.1. Objective 1 Results: Aviation Data Sources and GIS Integration Considerations

3.1.1. Data Sources for Airspace

The GIS framework successfully consolidated multiple aviation datasets, including FAA aeronautical layers, UAS Facility Map data, ADS-B data, and UAS flight logs, into a unified geospatial environment suitable for both 2D and 3D airspace analysis. All airspace datasets imported from the FAA ADDS and UDDS were validated, reprojected when necessary, and converted into standardized feature classes that enabled consistent spatial referencing across the research area.

Curated reference layers from Esri’s Living Atlas required minimal preparation and served as accurate basemap and boundary datasets. In contrast, downloaded FAA shapefiles and tabular UAS flight log data required varying levels of preprocessing, including attribute alignment, format standardization, and verification of coordinate systems. These steps ensured that Class Airspace boundaries, Special Use Airspace, airport footprints, and LAANC UAS Facility Map grids were accurately represented and spatially interoperable.

The integration process produced a complete and high-fidelity airspace dataset for the case study region. The final airspace layers, which included Class D and Class E airspace boundaries for KLAF and KLAF LAANC polygons, were successfully extracted, cleaned, and incorporated into the geodatabase. This allowed precise overlay of ADS-B trajectories and UAS flight paths. The standardized data environment supported reliable spatial queries, conflict zone identification, and detailed visualization of vertical and horizontal interactions within the NAS.

Collectively, these results demonstrate that the diverse data sources described in Section 2.2.1 can be effectively harmonized within a single GIS workspace. This unified spatial framework supported the subsequent 2D and 3D modeling, temporal analysis, and safety assessment presented later in this study.

3.1.2. Data Sources for ATM/UTM

The GIS framework successfully integrated all available ATM and UTM data sources into a unified analytical environment. Crewed aircraft data for the “DJI M300 RGB and PDU57 Interaction Case Study” were obtained from a local dump1090 ADS-B receiver and were incorporated into the GIS after validating file structure, coordinate formatting, and temporal completeness. The ADS-B data initially provided by the DJI M300 observer contained only a short segment of the PDU57 aircraft track, consisting of approximately 38 s (77 position reports). To ensure a complete representation of the PDU57’s activity, the full 24 h log from a local dump1090 ADS-B receiver was processed. This dataset included multiple sorties for PDU57 and provided a comprehensive flight path for the sortie associated with the interaction event.

The internally sourced UAS data were derived from the DJI M300 Flight Record provided by the DJI observer in spreadsheet .CSV format. The file contained detailed positional information for both the aircraft and the controller (operator), allowing full reconstruction of UAS movement and operator location throughout the mission. The observer also provided a photograph of the interaction event and a map-based location reference, which served as supporting spatial context within the GIS environment. No external UAS detection systems, such as DJI AeroScope or Remote ID receivers, were available for this case study, and therefore the UAS analysis relied entirely on internally generated system data.

All ATM and UTM datasets were successfully imported into the WGS 1984 Web Mercator (auxiliary sphere) projection used for the case study, and no unit conversions were required because each dataset was already provided in imperial units. This projection was selected because the data were also utilized in ArcGIS Online, which uses the same projection for its default basemaps and tiling scheme, ensuring compatibility with other web mapping services such as Google Maps. The WGS 1984 Web Mercator (auxiliary sphere) projection provides a standardized framework for consistent visualization across platforms. While this is a non-conformal projection and introduces minor distortions, the scale and spatial extent of the case study minimize its impact on distance calculations, and all measurements were performed within the same projection framework to maintain consistency. The observer’s photograph and map-based position provided critical qualitative context for the temporal and spatial validation of the core event. Specifically, the photograph’s timestamp and implied perspective, along with the UAS Flight Record, were essential for isolating the correct aircraft “PDU57” track within the 24 h of raw dump1090 ADS-B data. The resulting data environment allowed precise comparison and measurements of ADS-B aircraft tracks, UAS flight paths, and the operator’s location, enabling the detailed spatial and temporal analyses presented later in the study.

Table 7. ATM/UTM geospatial datasets for DJI M300 RGB and PDU57 interaction case study.

ATM/UTM Geospatial Datasets for DJI M300 RGB and PDU57 Interaction Case Study
Data	File Type	File Size (KB)	Name	Dates	Duration	Reports/Lines
ADS-B Tracks	.TXT	178,575	dump1090-127_0_0_1 (Full Receiver Range)	0001 to 2359	24 h	2,061,405
	.CSV	188	data (Parser-Limited Geographic Area)	083728 to 211252	12 h 35 min	2233
DJI UAS Position Reports DJI Controller (Operator) Position Reports	.CSV	36,175	DJIFlightRecord_(16-28-15) DJIFlightRecord_(16-57-58)	162815 to 172618	58 min	29,479
DJI M300 Observer Photo	.JPG	232	170525	170525	N/A	N/A
DJI M300 Observer Position	.PNG	770	Screenshot 2024-05-29 101018	170525	N/A	1

contains a detailed summary of the ATM/UTM geospatial datasets utilized in the case study.

3.1.3. Altitude Conversions

The altitude environment for the case study was standardized by integrating FAA airspace guidance with the reported altitude values from the crewed aircraft and the UAS. To establish the necessary vertical boundaries for GIS layers, the study utilized charted altitude limits derived from FAA charting products, VFR/IFR Charts, and Electronic Flight Bag (EFB) references such as ForeFlight.

Table 8. Altitude conversions for comparative analysis [43].

Airspace	Feet Bottom	Shelf	Feet Top
Class A	18,000 MSL		60,000 MSL
Class B	Surface	2+ Layers	10,000 MSL
Class C	Surface	1200 AGL	4000 AGL
Class D	Surface		2500 AGL
Class E	700 AGL		17,999.99 MSL
	1200 AGL		17,999.99 MSL
	14,500 MSL		17,999.99 MSL
Class G	Surface		700 AGL
	Surface		1200 AGL
	KLAF	Field Elevation	606 ft		Flight Chart Data for KLAF
Airspace	Feet Bottom	Shelf	Feet Top		Feet Bottom	Shelf	Feet Top
Class D	606 FT		3106 FT	Class D	Surface		3100 MSL
Class E	606 FT		17,999.99 FT	Class E	Surface		17,999 MSL
Class G	606 FT		1306 FT	Class G	Surface		1306 MSL

summarizes the standard FAA altitude structure for controlled and uncontrolled airspace and provides the specific values applied to the study area centered on KLAF. The FAA classifies the airspace into the following five main types:

• Class D (Controlled): At KLAF, where the field elevation is 606 feet MSL, the Class D airspace extends from the surface up to 3106 feet MSL (or 2500 feet AGL). This defines the immediate controlled environment for the airport.
• Class E (Controlled): This class typically begins at 700 feet AGL or 1200 feet AGL and extends up to 17,999.99 feet MSL. Near KLAF, the Class E floor is at the surface (606 feet MSL), providing controlled protection for approach and departure procedures.
• Class G (Uncontrolled): Uncontrolled airspace exists from the surface up to the floor of the overlying controlled airspace. At KLAF, this Class G airspace extends from the surface (606 feet MSL) up to 1306 feet MSL (700 feet AGL).
• These specific altitude shelves, alongside the LAANC grid and nearby Special Use Airspace, formed the foundation for the Class D, Class E, and Class G GIS layers created for the study area, enabling precise vertical analysis of the aircraft tracks.

The altitude environment for the case study was standardized by prioritizing GNSS-derived geometric altitude for both platforms. For the PDU57 aircraft, while barometric altitude allowed for general alignment with modeled Class D and E elevation limits based on the KLAF field elevation of 606 feet, the final analysis utilized the geoaltitude reported via ADS-B to ensure a GNSS-based reference. Similarly, although the DJI M300 Flight Record indicated a takeoff elevation of 697 feet and reported height relative to that baseline, the separation analysis utilized the RTK-corrected ellipsoidal height to maintain vertical precision.

By using these geometric altitude references for both aircraft, the framework established a synchronized vertical baseline that eliminated the inherent atmospheric and instrumentation errors common to barometric reporting. This consistency was essential for resolving discrepancies between different altitude baselines and provided a high degree of confidence in the final calculated separation distances.

The differences in how systems report altitude underscore the importance of understanding each platform’s sensors and data sources. Establishing a consistent altitude framework prevented the introduction of unnecessary errors and ensured accurate vertical alignment between aircraft tracks and airspace layers. The use of a conversion worksheet, or equivalent method, proved beneficial for organizing these values and is especially important when working across both imperial and metric units or when combining altitude references based on distinct baselines.

3.1.4. Time Conversions for Animation

Accurate temporal representation was essential for visualizing the movement of both the crewed aircraft and UAS within the GIS environment. To facilitate the creation of spatial animations, time data from both the ADS-B and DJI M300 datasets was converted into a consistent format compatible with the GIS. Specifically, a “TimeConversion” field was added to each dataset, ensuring the time format adhered to the “YYYYMMDDhhmmss” standard, which supports detailed animation at the second level with Esri’s ArcGIS Pro.

The time conversion process enabled the precise synchronization of position reports from both aircraft types, facilitating their integration into 2D and 3D spatial models. This temporal alignment was critical for modeling the dynamic interaction between the crewed aircraft and UAS as they navigated through multiple airspace layers and altitude blocks during their flights.

While the time data for both the ADS-B and DJI M300 Flight Records was manually processed in Microsoft Excel to ensure compatibility with the GIS platform, advanced Python scripting could also be employed for handling non-standard time formats, after incorporation into the GIS itself. This flexibility ensures that diverse data sources can be integrated effectively into the GIS framework, supporting further analysis and visualization.

Once the time data were standardized across datasets, the resulting aviation data were fully prepared for spatial analysis. This allowed for seamless animation and interaction modeling, enhancing the understanding of flight paths and airspace usage in the context of case study.

3.2. Objective 2 Results: GIS Methods for Modeling and Analyzing UAS-Crewed Aircraft Interactions

3.2.1. Airspace

The creation of accurate 2D and 3D airspace visualizations was fundamental to analyzing the UAS and crewed aircraft interactions within the study area. Data from KLAF’s Class D and E Airspaces, surrounding Special Use Airspace, and LAANC blocks were transformed into distinct feature layers. These layers were segmented by altitude, enabling both horizontal and vertical analysis of aircraft movements.

For 3D visualization, airspace polygons were extruded along the z-axis (elevation–height/altitude) to represent different altitude blocks. Each layer’s base and maximum altitude were defined as fields in the Attribute Table, allowing for accurate vertical representation within the GIS. Elevation properties were configured to ensure the correct display of these layers in both SI and NIST units.

The interpretation of VFR and IFR flight paths further enhanced the spatial analysis by providing context for aircraft movements within the designated airspace volumes. Flight paths for both crewed aircraft and UAS were overlaid onto the airspace layers, facilitating the identification of potential conflict zones. Spatial analysis determined where aircraft trajectories intersected with airspace boundaries, highlighting areas of potential interaction.

This approach enabled the identification of operational corridors and safety risks associated with UAS and crewed aircraft interactions, particularly in areas where airspace blocks overlapped or where aircraft navigated close to controlled or Special Use Airspace, such as aerodromes like KLAF. The resulting 2D and 3D models provided critical insights into airspace usage and potential areas for deconfliction in shared airspace environments.

3.2.2. ATM/UTM

The DJI M300 Flight Record data, Figure 4, was used to model the UAS flight path in a 2D map, representing the full 58 min mission through position reports. To distinguish between the operator’s movements and the UAS itself, the DJI M300 controller (operator) position reports were isolated into a separate feature layer, while the relative position of the observer at the time of the photo was represented as a single point. This separation allowed for clear visualization of both the operator’s movements and the UAS path.

Similarly, the Local Dump 1090 and ADS-B parser data, Figure 5, provided insight into crewed aircraft flights in the region, with a 24 h dump1090 ADS-B receiver dataset overlaid by a subset of position reports obtained from a limited geographic range and time period. This 2D map highlighted the merging flight paths as crewed aircraft approached KLAF, and it demonstrated the utility of attribute queries, spatial joins, and data exports to isolate data by aircraft type, location, or time, thus enabling focused analysis on specific interactions between UAS and crewed aircraft.

For the 3D scene of the DJI M300 RGB and PDU57 interaction, Figure 6 was created showing the extent of dump1090 ADS-B position reports for PDU57 throughout the day as compared to the DJI M300 RGB mission. Despite the short duration of the interaction, the 3D scene allowed for an understanding of PDU57’s operational pattern or “Pattern-of-Life” analysis. PDU57 was engaged in pilot training to the South, at one point completing steep turns, figure eights, and likely various holding patterns; while prior to the interaction the flight was out at the “Boiler” VHF Omnidirectional Range/Tactical Air Navigation (VORTAC) (BVT) in holding patterns followed by an approach with an extended circle to land or downwind leg to the East prior to landing on Runway 23. Figure 7 provides an alternate perspective of the model from the East to highlight PDU57’s flight path throughout the interaction and further emphasize the critical spatial and temporal dynamics of the event.

Once the UAS and crewed aircraft interaction was modeled in both 2D maps and 3D scenes, spatial analysis was conducted to quantify distances, angles, and separation between the aircraft. This approach was particularly valuable for examining critical moments of the interaction, such as when the DJI M300 entered the PGPS mode, triggering an avoidance maneuver as PDU57 passed over the mission area. Both geodesic and planar measurements were employed to ensure precise calculation of separation distances and interaction angles.

For enhanced visualization, a dynamic animation of the KLAF 3D scene was created by the author on 2 June 2024 (available at YouTube link: https://youtu.be/5GzxfbqvmYc), capturing the UAS and crewed aircraft interaction between 17:00 and 17:10.

Across all datasets, standardization of Coordinate Reference Systems, units of measure, and temporal formats ensure analytical consistency and reliability. Geospatial workflows, including data cleaning, conversion, and layering, facilitated the integration of multiple sources, while spatial and temporal filtering allowed focus on the interaction of interest without restricting applicability to specific GIS platforms.

Overall, integrating ATM/UTM data within 2D and 3D GIS environments provide a comprehensive understanding of UASs and crewed aircraft interactions. The combination of advanced geospatial processing, time-series visualization, and elevation-based analysis enabled detailed assessment of dynamic interactions and the supported evaluation of potential conflicts in shared airspace.

3.2.3. Geoprocessing Tools

A range of geoprocessing tools were employed to analyze the interaction between the DJI M300 UAS and PDU57 crewed aircraft. While this case study utilized the 3D Analyst and Analysis toolboxes,

Table 9. List of geoprocessing tools for analysis of M300 RGB and PDU57 interaction case study.

List of Geoprocessing Tools for Analysis of M300 RGB and PDU57 Interaction Case Study
ArcGIS Pro—2D Maps and 3D Local Scenes
Toolbox	Toolset	Tool	Details
Three-Dimensional Analyst Tools	Three-Dimensional Proximity	Near 3D	Calculated 3D distances between DJI M300 and nearest PDU57 ADS-B position reports. Utilized the Measure feature to further refine points based on time
Analysis Tools	Proximity	Near	Calculated 3D distances between DJI M300 and nearest PDU57 ADS-B position reports. Utilized the Measure feature to further refine points based on time
	Statistics	Summary Statistics	Utilized to find statistical information on speed, altitude, and times for DJI M300 UAS and PDU57 aircraft data

, the methods described are applicable across GIS platforms, including both proprietary and open-source software. Key tools enabled the calculation of 3D distances, angles, and proximity relationships, and the generation of summary statistics for speed, altitude, and temporal attributes. These analyses were essential for quantifying critical interaction metrics.

Figure 8 and Figure 9 illustrate representative still frames from 2D maps and 3D scenes demonstrating the spatial context of the interaction. The combination of 2D and 3D visualization allowed for detailed examination of both horizontal and vertical separation, as well as pattern-of-life analysis for PDU57 flights in the surrounding airspace.

Although additional toolboxes, such as Aviation and Spatial Statistics, offer capabilities for analyzing airports, obstacles, or flight path clustering, their utility for directly assessing aircraft interactions was limited. Nevertheless, these toolsets support supplemental analyses, including spatial joins, airspace association, and pattern identification. Open-source platforms such as QGIS continue to expand capabilities in these areas, ensuring broader accessibility for similar research workflows.

Overall, the integration of geoprocessing tools with 2D and 3D spatial modeling provided a robust framework for quantifying UAS and crewed aircraft interactions, supporting dynamic visualization, spatial measurement, and temporal analysis. These methods facilitate a deeper understanding of operational behaviors, potential conflict hotspots, and the effectiveness of GIS-based approaches for airspace safety analysis.

3.2.4. Narrative/Timeline

The narrative and timeline to follow are the culmination of the “DJI M300 RGB and PDU57 Interaction Case Study” research which required both the quantitative data, as well as intensive qualitative study of the 2D map and 3D scene models, animation, FAA charts (VFR, IFR, Approach Plates, etc.) to ensure a thorough analysis was conducted.

At 4:28:15 PM, a Purdue University DJI M300 started a mapping mission near the Purdue water tower on the Northwest side of campus. A normal (lawnmower) pattern was flown with West-to-East and East-to-West tracks moving from North to South. The UAS crew had been approved for an altitude waiver above the four LAANC boxes ranging from 0 to 100 to 200 (×2) in which it would fly.

• At 5:02:09 PM, a Purdue University Piper PA-28-181 Archer TX, callsign “PDU57”, entered KLAF Class D airspace Inbound to the Southeast.
• At ~5:04:39 PM, PDU57 turned to the East for what appeared to be an extended circle to land or downwind to land on Runway 23 at KLAF.
• At 5:05:09 PM, PDU57 was tracking 076 degrees with a groundspeed of 97 KTS at 1250 FT barometric altitude (~644 FT AGL… noting KLAF field elevation 606 FT). DJI M300 1047 FT altitude (350FT AGL… noting the DJI utilized the launch point elevation for reference, in this case 697 FT).

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  Lateral separation: 2317.35 FT (geodesic) or 3040.42 FT (planar)

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  Vertical separation: Based on reported AGL altitude calculations ~203 FT to ~294 FT

• Prior to 5:05:09 PM, the DJI M300 was operating off fly state “waypoints” at an altitude of 1047 FT (350 FT AGL… noting the DJI utilized the launch point elevation for reference, in this case 697 FT) as planned in the mapping mission. At 5:05:09 PM, the operator became concerned with the approaching aircraft and suspended the mission, changing the fly state to “P-GPS.” At 5:05:11 PM, the UAS began an 11 s descent to increase vertical separation down to an altitude of 911 FT (213.5–213.9 FT AGL) by 5:05:22 PM.
• 5:05:23 PM Closest point of interaction (lateral).

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  PDU57 1250 FT barometric altitude (~644 FT AGL… noting KLAF field elevation 606 FT).

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  DJI M300 911 FT altitude (213.9FT AGL… noting the DJI utilized the launch point elevation for reference, in this case 697 FT).

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  Lateral separation: 458.94 FT (geodesic) or 603.84 FT (planar)

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  Vertical separation: Based on reported AGL altitude calculations ~339 FT to ~430.1 FT

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  The DJI M300 remained at this location and altitude until 5:06:39 PM when the aircraft had passed well clear, and then the operator resumed the mission back to a fly state “waypoint”.

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  DJI M300 remained in PGPS mode for 1 min 30 s (5:05:09 PM to 5:06:39 PM) (907 position reports).

• At 5:06:39 PM, PDU57 was ~1623 FT from the threshold on final approach to runway 23 at 725 FT barometric altitude (~119 FT AGL… noting KLAF field elevation 606 FT). DJI M300 911 FT altitude (213.9FT AGL… noting the DJI utilized the launch point elevation for reference, in this case 697 FT).

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  Lateral separation: 5703.34 FT (geodesic) or 7510.76 FT (planar)

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  Vertical separation: Based on reported AGL altitude calculations ~94.9 FT to ~186 FT

• At 5:07:10 PM, PDU57 reported its last ADS-B track upon landing rollout on Runway 23 at KLAF.

An interactive StoryMap, “The Skies Above,” was created to visualize the DJI M300 and PDU57 interaction case study using 2D maps and 3D scenes. The author presented the following StoryMap on 14 May 2025 at the Indiana Geographic Information Council (IGIC), 2025 GIS Conference, which provides a dynamic representation of the spatial and temporal analysis presented in this study, enabling users to explore key aspects of UAS and crewed aircraft interactions (https://storymaps.arcgis.com/briefings/99d5bd57de3442cabc5d8e2d287a4207).

4. Discussion

The mitigation efforts currently employed to reduce UAS, and crewed aircraft interactions rely on outdated strategies and non-integrated technologies. Findings from this research and the “DJI M300 RGB and PDU57 Interaction Case Study” confirm that a Geographic Information System (GIS) has strong potential as a centralized hub for collating, analyzing, modeling, and visualizing crewed aircraft, UASs, and Advanced Air Mobility (AAM) operations on a single platform to address National Airspace System (NAS) Air Traffic Management/Unmanned Traffic Management (ATM/UTM) integration deficiencies.

When paired with advancements in sensor technologies and AI, GIS can mitigate challenges related to congested airspace and the integration of UAS and AAM. As Hupy and Lercel note, GIS contributes to the FAA’s Risk-Based Decision Making (RBDM) Strategic Initiative, a crucial aspect of the 2017 Integrated Oversight Philosophy (IOP) identifying safety oversight systems for the rapidly changing National Airspace System (NAS) [44]. A GIS can spatially quantify and measure the separation of three-dimensional position reports for UAS Radio Frequency (RF) and crewed aircraft Automatic Dependent Surveillance–Broadcast (ADS-B) data [44]. GIS stands to provide not only a means to model and analyze historic aviation data, but it also has the potential to function as platform for real-time visualizations and analytics of multiple data streams within the digital transformation of aviation data sources [45].

The proposed GIS-based methodological framework could theoretically operate in near real-time; however, achieving this capability would require substantial collaboration among researchers, regulatory agencies, and industry partners across multiple disciplines. At a fundamental level, such a system would require the integration of FAA data streams within a three-dimensional GIS environment, including z-axis elevation attributes and terrain elevation models (DEMs) appropriate to the operational airspace. Implementing the workflows described in this study for near real-time use would necessitate the development of custom algorithms capable of ingesting, processing, and analyzing streaming aviation data, as well as the training of AI models to interpret spatial outputs and generate timely alerts or advisories for operators. Scaling this approach to hundreds or thousands of simultaneous crewed aircraft and UAS operations, as would be required in a full ATM/UTM context, would further demand robust data pipelines, high-performance computing resources, and automated monitoring systems to maintain situational awareness and data integrity across multiple airspace sectors. Realizing this vision would depend on coordinated partnerships among regulatory authorities and industry stakeholders with expertise in air traffic control, aircraft surveillance systems, sensor technologies, and GIS-based analytics to ensure interoperability, operational relevance, and safe management of dense airspace. Organizations such as Esri, ArcGIS Velocity, and FlightAware are already collaborating and exploring real-time solutions that could inform these scaling efforts [12].

Future research seeks to incorporate near real-time Application Programming Interfaces (APIs) incorporating additional aviation data sources to enable scalable, multi-airport studies. This integration could support the development of AI-driven advisory systems for ATC, crewed aircraft, and UAS operators that emphasize anomaly detection (“Outliers”) rather than routine operations. This research has the potential to analyze and recognize spatial patterns, thus develop “Patterns-of-Life” for crewed aircraft and UAS that can train AI to recognize these routine operations based on flight profiles, airspace classes, VFR/IFR flight routes, airport arrivals/departures, and traffic conditions. A future AI-driven advisory system could then provide alerts to operators when air traffic deviates from expected patterns, is non-participatory, or presents a potential conflict.

Future AI-driven advisory systems could leverage the spatial context provided by GIS to improve anomaly detection. While the reactive Resolution Advisories (RA) of TCAS provide vertical maneuvering instructions when a collision is imminent, a GIS-based UTM advisory system functions more akin to a predictive Traffic Advisory (TA). By recognizing “Patterns-of-Life,” the system provides the operator with early situational awareness, identifying hazardous trajectories well before they infringe upon the 500-foot NMAC volume. This process is enhanced by adopting specialized evaluation methods for potential collisions and structured warning protocols that detect conflicts earlier than traditional onboard sensors [46,47]. This proactive approach allows for earlier, less aggressive deconfliction, reducing the need for emergency pilot intervention and moving beyond the platform-specific, reactive nature of current collision avoidance technologies.

Importantly, this approach is UTM-centric, focusing on networked, system-level situational awareness and early warning across the airspace, rather than the platform-specific approach typical of current BVLOS technologies, which emphasizes individual aircraft track awareness and reactive conflict avoidance. A UTM-focused system provides proactive advisories intended to reduce reliance on reactionary operator decisions and enhance coordinated airspace safety.

While the current case study validates the GIS framework through a UAS and fixed-wing interaction, the methodology is designed to be platform-agnostic, supporting the integration of helicopters and eVTOL aircraft that frequently operate in low-altitude urban environments. Future research will expand this framework to include these diverse AAM platforms, utilizing GIS-driven spatial analysis to visualize flight risks in complex metropolitan areas. Specifically, the integration of Evolutionary Algorithms for 3D flight route optimization, as demonstrated in recent AAM studies, could be paired with this GIS framework to enhance the safety and efficiency of air-taxi corridors within the NAS [48].

Policy-oriented research priorities and practical applications should emphasize areas that bridge technical innovation with operational safety and regulatory decision making. The proposed GIS-based framework offers actionable tools for both regulators and UTM service providers to enhance situational awareness, risk management, and proactive airspace oversight:

• Visualization of airspace conflicts and traffic patterns: Develop GIS-based visualizations, including temporal animations, heatmaps, and 3D models, to illustrate traffic density, potential conflicts, and areas of regulatory concern. Regulators and UTM providers could use these tools to monitor real-time and historical traffic patterns, identify congestion hotspots, and prioritize oversight or intervention.
• Analysis of airspace violations and safety risks: Use GIS to identify and assess historical and near-real-time violations, informing targeted safety measures, enforcement strategies, and operational policy. This enables regulators to take data-driven actions, such as issuing dynamic airspace restrictions or conducting focused safety campaigns.
• AI-enhanced advisory systems: Leverage GIS spatial context to improve AI detection of abnormal flight behavior, enabling actionable advisories for operators and supporting regulatory oversight. For UTM providers, these systems can provide early alerts when aircraft deviate from expected patterns, helping mitigate potential conflicts before they arise.
• Foundations for real-time risk prediction: Model spatial–temporal risk factors to inform future predictive safety systems and policy frameworks for dynamic airspace management. Regulators could apply these models to evaluate proposed BVLOS operations, manage multi-airport traffic, and guide the integration of new UAS and AAM operations into the NAS.

Collectively, these priorities demonstrate how the GIS-based framework can translate complex aviation data into practical, operational tools that enable regulators and UTM service providers to move from reactive oversight to proactive airspace management.

Scaled, long-term case studies at major airports can validate these approaches by identifying hotspots for UAS/crewed aircraft interactions. The resulting insights could inform “Notice to Airmen” (NOTAMs), Electronic Flight Bag (EFB) updates, and enforcement strategies, enhancing situational awareness and regulatory compliance.

In summary, integrating GIS, AI, and advanced sensor networks offers a transformative opportunity to modernize ATM/UTM operations. Continued research in these areas is critical to improving safety, efficiency, and oversight in the NAS and to preemptively address risks before major airspace incidents occur.

5. Conclusions

This research demonstrates that a GIS framework serves as a robust, centralized platform for addressing current deficiencies in ATM/UTM integration within the NAS. By applying a multi-phase methodology to the “DJI M300 RGB and PDU57 Interaction Case Study,” this study successfully achieved the following research objectives outlined in the Introduction.

5.1. Achievement of Objectives

Objective 1: Data integration and preprocessing: The framework successfully harmonized heterogeneous aviation datasets, including ADS-B for crewed aircraft, UAS flight logs, and FAA aeronautical data. The methodology established essential preprocessing standards for resolving altitude referencing discrepancies (barometric vs. height above takeoff) and standardizing temporal formats into a structure required for precise spatiotemporal synchronization.

Objective 2: Modeling and quantitative analysis: The study developed and validated GIS-based methods to model interactions in 2D and 3D environments. Utilizing core geoprocessing tools, the framework enabled the precise measurement of spatial separation during a critical near-miss event, identifying a minimum separation of approximately 459 feet laterally and 339 feet vertically.

5.2. Summary of Implications

The case study at Purdue University Airport (KLAF) confirms that GIS allows for the visualization of dynamic aircraft trajectories and “Pattern-of-Life” analysis that traditional, disconnected systems lack. While results must be interpreted within the inherent positional uncertainties of GNSS-based systems, the framework provides a scalable foundation for modernizing NAS oversight.

When integrated with AI for anomaly detection and advanced sensors, this GIS-driven approach offers a transformative path toward real-time risk prediction and dynamic airspace management. These findings provide actionable tools for regulators and UTM service providers to transition from reactive safety oversight to proactive, data-centric management of shared airspace.

5.3. Future Research Directions

Future continuation of this work should prioritize the transition from post-flight analysis to near real-time operational oversight. This includes the development of custom algorithms and APIs to ingest live data streams, paired with AI models trained to recognize routine “Patterns-of-Life” and detect hazardous anomalies or non-participatory aircraft. Building on recent safety research, these systems must integrate standardized conflict evaluation methods to calculate collision probabilities and generate tiered warning alerts, such as those utilizing Support Vector Machines (SVM) for low-altitude conflict network modeling or data-driven approaches for predicting accident severity [46,47]. Additionally, scaling this framework to encompass multi-airport studies and BVLOS operations will be critical for informing NOTAMs and predictive safety systems. Such systems could leverage geometric and machine learning-based techniques for Conflict Detection and Resolution (CD&R), alongside 3D-flight route optimization for AAM platforms in urban environments. Such advancements will facilitate a shift from reactive oversight to proactive, dynamic airspace management, ensuring the safe integration of UAS and AAM platforms into the increasingly congested NAS.