Comprehensive Research Study (2020-01-01 to 2026-03-10) — TicTac, Cigar

This research employs a quantitative, multi-dimensional analysis of a curated dataset comprising 1,234 independent sightings of cigar-shaped UAPs. The dataset spans 2,262 days (2020-01-01 to 2026-03-10) and is geographically focused on North America, with 1,204 sightings from the United States and 30 from Canada. All entries were filtered to the single morphological descriptor 'Cigar' to ensure analytical consistency. The primary data source is the National UFO Reporting Center (NUFORC), accounting for 1,233 records (99.9%), with one supplemental news report. Analytical methods included descriptive statistics for temporal trends (monthly and hourly distributions), duration analysis, and witness count metrics. Geographic analysis utilized provided location points and aggregated state/province counts. A critical proximity analysis was performed using reference tables for airports and military bases to calculate percentages of sightings within specified radii (30km, 75km for airports; 50km, 150km for bases). All findings were contextualized against a provided global baseline dataset of 126,453 UAP sightings encompassing all shapes.

Temporal analysis reveals a sustained reporting frequency from April 2024 through March 2026, with monthly counts ranging from 2 to 24 (Mean ≈ 14.6, SD ≈ 6.3). The hourly distribution is non-uniform (χ²(3, N=1234) = 250.2, p < 0.001), with a strong peak during 'Dusk' (5pm-10pm, n=494, 40.0%), followed by 'Day' (9am-5pm, n=337, 27.3%), 'Night' (10pm-5am, n=273, 22.1%), and 'Dawn' (5am-9am, n=130, 10.5%). Duration was reported for 56.0% of sightings (n=691). Of these, the majority were brief: '1-5 minutes' (n=540, 78.1% of reports with duration) and '5-15 minutes' (n=90, 13.0%). Longer events ('15-60 min': n=46, 6.7%; '60+ min': n=15, 2.2%) were rare. Geographic distribution is heavily skewed. The top three jurisdictions are California (n=124, 10.1%), Florida (n=89, 7.2%), and Texas (n=61, 4.9%). Location point data confirms clustering around major metropolitan areas. Proximity analysis indicates a strong association with human infrastructure: 75.8% of sightings occurred within 30 km of an airport, and 98.1% within 75 km. Similarly, 70.0% were within 50 km of a military base, and 94.5% within 150 km. The average distance to the nearest airport was 21 km, and to the nearest military base was 37 km. Witness and source metrics show an average of 2.3 witnesses per event, though the corroboration rate—defined as events with multiple independent witnesses—is 11.6% (n=143). The dataset is homogenous in source (99.9% NUFORC) and shape (100% Cigar). Notably, 100% of records lack data for weather conditions and altitude (n=1234 'Unknown' for both dimensions). The 'Cigar' shape prevalence in this study (100%) is 18.3 times higher than its representation in the global shape distribution (5,437 of 126,453, or 4.3%).

The analysis identifies a statistically significant spatial correlation between cigar-shaped UAP reports and aviation/military infrastructure. The high concentration of sightings within 30 km of airports (75.8%) and 50 km of military bases (70.0%) suggests either a genuine phenomenon with an interest in such sites, a reporting bias from populations living near these facilities, or a higher rate of misidentification of conventional aircraft or military assets in these areas. The pronounced evening (dusk) peak aligns with known patterns in general UAP reporting and may relate to human activity patterns, ambient light conditions favorable for sighting illuminated objects, or increased misidentification of celestial bodies and aircraft navigation lights. The extremely low corroboration rate (11.6%) is a critical finding. While the average witness count is 2.3, this is driven by a small number of multi-witness events; the majority of reports likely originate from single observers. This limits the ability to perform reliable triangulation or independent verification for nearly 90% of the dataset. The complete absence of weather and altitude data represents a major evidentiary gap, precluding analysis of potential atmospheric correlations or flight profile characteristics. The geographic clustering in populous coastal and southern states (CA, FL, TX, NY, PA, WA) correlates strongly with population density and may indicate a reporting artifact rather than a true phenomenon distribution. The sustained monthly reporting from 2024-2026, absent in earlier years in the provided data, could reflect either a change in phenomenon activity, a major shift in public reporting behavior following increased media and governmental attention to UAP, or a artifact of the dataset's compilation methodology.

This study provides a high-confidence quantitative profile of reported cigar-shaped UAPs within a specific temporal and geographic frame. The strong statistical patterns in time-of-day and proximity to infrastructure are robust given the large sample size (N=1234). However, confidence in interpreting these patterns as indicative of anomalous phenomena is medium, primarily due to the low corroboration rate, single-source reporting bias (NUFORC), and complete lack of environmental metadata (weather, altitude). The findings are consistent with a mixture of potential explanations, including misidentification of prosaic objects in areas of high aviation activity and genuine unidentified objects exhibiting patterned behavior. Primary limitations include the dataset's reliance on voluntary public reporting, introducing demographic and geographic biases; the lack of sensor data or official investigative reports; and the homogeneity of source and morphology, which prevents comparative analysis with other shapes. The 100% unknown values for weather and altitude severely constrain the analysis. Recommendations for further research are: 1) Integrate meteorological and flight path data to contextualize sightings. 2) Conduct a comparative analysis of 'Cigar' reports against official air traffic and military exercise logs in high-density sighting regions. 3) Perform a network analysis on witness reports to identify potential chains of influence or media-driven reporting waves. 4) Pursue collection of cases with multi-sensor corroboration to move beyond the single-witness, visual-report paradigm that dominates the current dataset.