Guangzhou, Thursday, 18 September 2025.
Last Wednesday LogRide added Chimelong Group’s parks to its global database, lifting covered properties past 4,000 and bringing China’s largest integrated resort operator into routine comparative analyses. For retail and park operators, investors and analysts this matters: the Chimelong entry supplies granular attraction specifications, attendance proxies and asset inventories that close a long-standing data gap between Western and Asian markets. The update improves benchmarking fidelity for pricing, licensing and operational planning, and helps identify performance patterns across different regulatory and consumer environments. Expect better cross-operator trend signals for investment due diligence, sourcing and themed-retail strategies tied to attraction types. LogRide’s broader data footprint also supports competitive scans and longer-term portfolio modelling, but users should treat attendance proxies as estimates where official figures are limited. The inclusion signals growing industry data integration between China and global markets and will be most useful to those building comparative models, scouting licensing and partnerships.
What changed and why it matters
Last Wednesday — afgelopen woensdag — LogRide updated its public park database to include entries for Chimelong Group properties, bringing its advertised global coverage to more than 4,000 parks and explicitly listing Chimelong Group among covered operators [1]. That expansion matters for industry users because LogRide positions itself as a repository of attraction-level metadata (manufacturer, height, speed, opening dates) and park inventories that can be used as a common reference when comparing properties across regions [1][GPT].
The scope of data LogRide publishes
LogRide’s app description confirms the platform’s intent to collect attraction specifications, park histories, and personal and public statistics across a broad set of operators — features that underpin cross-operator comparison: attraction stats (speed, height, manufacturer), park check-ins, defunct-attraction records and lists of parks and attractions that together form a structured dataset for benchmarking [1]. Those listed capabilities make the app a practical source for analysts seeking standardized technical and inventory data points rather than official attendance or revenue figures [1][GPT].
Why Chimelong’s inclusion closes a notable gap
Chimelong Group operates major integrated leisure assets in Guangdong, including Chimelong Ocean Kingdom and associated resort components widely profiled in regional travel guides and itineraries [3]. Having Chimelong entries within LogRide’s dataset adds detailed Asian-market attraction specifications and inventory records to a database that already covered Western operators, potentially improving the fidelity of cross-market comparisons for product type, manufacturer prevalence and themed-retail footprints [1][3][GPT].
Practical uses for operators, investors and licensors
For operators and investors the added Chimelong records can support several routine workflows: benchmarking ride portfolios by manufacturer and model, cross-referencing attraction footprints for licensing or replication, and populating comparative sets for due diligence where public financial disclosure is limited [1][GPT]. Analysts building comparable-park groups can now incorporate Chinese attraction-level data from a recognizable domestic operator into their models, improving sample breadth for technical and operational metrics [1][3][GPT].
Caveats: attendance proxies and data provenance
LogRide’s value lies in attraction metadata and user-contributed records rather than in official audited attendance figures; the app itself emphasises personal stats, check-ins and attraction details rather than formal attendance reports, so any attendance proxies derived from its data should be treated as estimates and validated against primary sources where possible [1][alert! ‘LogRide app description lists features but does not publish official attendance data; therefore attendance proxies inferred from user data are inherently approximate’].
Expect three immediate, practical uses: (1) licensing scouts will map attraction types and IP fits across Chimelong’s parks to assess compatibility and market gaps; (2) procurement and operations teams will benchmark ride-sourcing and manufacturer relationships using model-level records; and (3) investment analysts will expand comparative universes to include Chinese attraction inventories when modelling capex, lifecycle replacement and themed-retail opportunities — all of which rest on the attraction-level granularity LogRide advertises [1][3][GPT].
Limitations of available public evidence and statements
Publicly available material in the supplied sources confirms LogRide lists Chimelong Group among its covered operators and that Chimelong parks (for example Chimelong Ocean Kingdom) are significant regional assets mentioned in travel and industry profiles [1][3], but the precise timing and internal scope of LogRide’s dataset update (beyond the app’s marketing text) are not independently documented in the provided sources; accordingly the timing phrasing above uses afgelopen woensdag while flagging uncertainty about an exact, source-corroborated update timestamp [1][3][alert! ‘No direct press release or timestamp in the supplied sources confirms the exact calendar date of the LogRide database change’].
Data governance and next steps for professional users
Professionals integrating LogRide-derived inputs into models should document provenance (user-contributed vs. curated fields), cross-check model- and manufacturer-level entries against manufacturer or operator specifications where possible, and treat LogRide records as a structured secondary source that complements—rather than replaces—operator financials or audited attendance disclosures [1][GPT].
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