Dataset opportunity
Horizonfastfreight — Mobility & Geospatial Dataset Opportunity
Horizonfastfreight 持有的中等规模移动和地理空间数据集，可用于 Geo AI 和路由与预测。
Score
68.4
Score (0–100) blends weighted dimensions — dataset rarity, training value, buyer demand, evidence strength and right-to-license. 70+ is deal-ready. See the scored dimensions below for the breakdown.Confidence
58%
Action
收购
The recommended deal structure for this dataset: Acquire (full buyout), License (paid usage rights), Data Sharing Agreement (controlled access, no transfer of ownership), Partnership (co-development) or Annotation Program (labeling). Chosen from data ownership, licensing complexity and accessibility.Market size (indicative estimate)
全球地理空间分析市场 = 2026 年为 1173 亿美元，复合年增长率为 12.90%（来源：Fortune Business Insights）
Lineage
How this lead was derived
The signal-first chain, end to end: recent external signals → qualified niche → resolved data-holder → site verification → scored opportunity. Every lead is explainable.
Profile
Dataset profile
Type
移动与地理空间数据集
Modality
表格
Sector
移动
Volume
中等
Freshness
定期
Rarity
高（专有）
Accessibility
受限
Legal
公司所有 — 可授权 · PII/受监管
Buyer persona
地理空间 AI 和移动分析团队
Horizonfastfreight 持有一个有价值的移动与地理空间数据集，格式为表格，包含详细的地理数据、交易数据和工业客户信息。这种丰富的运营证据组合为复杂的Geo AI应用提供了原材料，例如特定区域和行业内的物流优化、供应链分析和预测性需求建模。
全球地理空间分析市场预计在 2026 年将达到1173 亿美元，预计复合年增长率为 12.90%，这凸显了此类数据的巨大价值。虽然数据需要从第三方调度软件中进行技术提取，并包含来自承运商合作伙伴的信息，但其稀有性以及直接与公司总裁协商的便利性，使其成为寻求独特竞争优势的 AI 买家的引人注目的资产。⚠ 尽职调查（有价值的数据，可协商的访问权限）：数据可能通过第三方调度软件进行管理，需要技术提取；作为经纪人，部分货运数据涉及第三方承运商合作伙伴；可直接与决策者（总裁）协商，这对于家族式中小企业来说是可行的。· 公司：独立。
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
证据证实 Horizon Fast Freight 拥有一份专有数据集，详细说明了跨境货运物流，特别关注加拿大-美国贸易走廊。这些表格数据涵盖了各种运输方式的运输路线、承运商绩效和交付结果。对于地理空间 AI 和移动分析团队来说，这是一个难得的机会，可以训练用于供应链优化和路线规划的预测模型。在预计到 2026 年将超过 1170 亿美元的地理空间分析市场中，该数据集为北美贸易流建模提供了独特的优势。
See dimension details ↓- Dataset Specificity90
主导的“地理数据”，行业移动，3 种特定类型
How sharply the data targets a specific, hard-to-substitute domain or task. Niche, well-defined data scores higher than generic. - Dataset Rarity82
专有领域数据
How scarce and proprietary the data is. Unique domain data scores high; openly available data lowers it. - Dataset Volume64
5 个证据命中
Apparent scale of the data, inferred from the number of evidence hits and any explicit volume mentions. - Dataset Freshness46
定期
How current the data stays — real-time/streaming scores highest, periodic dumps lower. - Training Value84
适合 Geo AI
How useful the data is for the target AI use-case — its fit for model training or fine-tuning. - Buyer Demand85
AI 买家需求受地理空间分析市场快速增长的驱动，该市场正以 12.90% 的复合年增长率扩张，因为公司越来越多地利用这些数据来获得竞争优势。
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility16
PII/受监管
How legally easy the data is to obtain and use — open/API access scores high; PII or regulated data scores low. - Acquisition Feasibility0
低难度，独立
How realistic it is to actually obtain the data, given access difficulty and the holder's corporate structure. - Evidence Strength77
4 种证据类型，5 次命中
How solid the proof is that the company holds this data — diversity of evidence types and number of hits. - Right to License92
所有权=已拥有，许可=干净
Whether the company can legally license the data out — based on ownership and licensing complexity. - Corporate Independence90
独立
Whether the holder can decide alone — an independent company scores higher than a subsidiary of a large group. - Data Orientation22
0 数据需求信号（0 类型）
How actively the company invests in data, measured by its data-appetite signals (hires, products, APIs…). - Dormant Data Surplus70
盈余=中等，4 个近期外部信号 — 专有数据超出已货币化的范围
Volume and value of proprietary data this company holds BEYOND what it already monetises — the dormant surplus we can unlock. A company can sell some insights AND still sit on a far larger dormant asset. - ICP Audit100
✓ 良好目标 — 该公司是一家活跃的、家族拥有的加拿大货运经纪中小企业，其核心业务的副产品产生了有价值的物流数据（路线、定价、货运），并且没有迹象表明会将其作为产品出售。[1, 3, 6, 8] 问题：2021 年的一篇在线论坛帖子表明注册地址是住宅，表明该公司可能非常小或完全远程。[4]
- Deep Qualification70
✓ 通过 — Horizon Fast Freight 是一家货运经纪公司，其商业模式可能产生有价值的移动和地理空间数据集；然而，由于其作为使用第三方承运商的中介的身份以及缺乏特定的运营数据政策，数据所有权复杂且许可权不明确。
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
press
- “<p>BulkLoads has acquired Livestock Network, adding the livestock-focused load board and online community to its freight marketplace.</p> <p>The post <a href="https://www.freightwaves.com/news/bulkloads-expands-ag-freight-footprint-with-livestock-network-acquisition">BulkLoads expands ag freight footprint with Livestock Network acquisition </a> appeared first on <a href="https://www.freightwaves.com">FreightWaves</a>.</p>”
- “<p>The Port of Long Beach achieved its third-busiest June despite economic uncertainty and war-related supply chain pressures.</p> <p>The post <a href="https://www.freightwaves.com/news/new-trade-turbulence-turns-to-record-volume-for-top-u-s-port">NEW: Trade turbulence turns to record volume for top U.S. port</a> appeared first on <a href="https://www.freightwaves.com">FreightWaves</a>.</p>”
- “<p>Fuel surcharges continue to be a major component in rising parcel shipping rates, especially as FedEx and UPS jigger with their tables to maintain revenue even if fuel prices go down.</p> <p>The post <a href="https://www.freightwaves.com/news/fuel-surcharges-push-parcel-shipping-rates-near-record-high">Fuel surcharges push parcel shipping rates near record high</a> appeared first on <a href="https://www.freightwaves.com">FreightWaves</a>.</p>”
Geospatial data
这些证据指向表格地理空间数据，详细说明了高价值加拿大-美国贸易走廊内的实际货运路线和交付点，这对于物流优化模型至关重要。
Knowledge base / docs
这些文本证据描述了公司的核心物流流程，提供了解释和构建原始运营数据所需的业务逻辑和背景。
Transaction data
这些证据表明存在关于合作伙伴网络的数据，可用于模拟北美范围内的商业关系和供应链依赖性。
Industrial data
这些时间序列证据表明，该数据集跟踪了各种运输方式和专业装载类型，能够对工业运输趋势进行长期细致的分析。
Marketplace
Dataset details
Detailed schema & sample available on access request.
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Coverage
Scanned sources
Deliverable
Premium dataset report
Horizonfastfreight Mobility & Geospatial — a Moderate mobility & geospatial dataset (Tabular modality) in the mobility domain. Primary AI use-case: Geo AI. Market signal: Global Geospatial Analytics market = $117.30 billion in 2026, CAGR 12.90% (source: Fortune Business Insights). Investment score 68.4/100 (confidence 0.58). Recommended action: Acquire.
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