Kogenta Connect FAQ.
Here are some commonly asked questions about Kogenta Connect and how things work.
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Kogenta Connect is a high-resolution, AI-enhanced geospatial data platform built on the Kogenta Grid – our uniform H3-based spatial framework. It fuses multiple data layers, movement signals, contextual indices, demographics and survey insights to deliver actionable intelligence across industries. An optional map-based visualisation platform known as Kogenta Explorer is also available to bring the data to life or the data layers can be loaded into your existing platform.
It eliminates fragmented data sources, inconsistent geographies, and legacy signal quality issues. Kogenta Connect provides a unified, trusted and explainable understanding of people, places, movement and context.
Our movement layers are created using AI models that ingest and synthesise multiple independent signals. No single dataset drives the output, giving high fidelity, strong robustness and a consistent hour-by-hour view of real-world activity. Our AI models have been tested against movement data truth sets of aggregated individual journeys collected via an Opt-IN SDK, consistently performing at 98.7% or higher accuracy.
- Advertising & Media Pitching, Planning and Activation
- Retail & Shopping Centres
- Cinema & Entertainment
- Local Government & Municipalities
- Real Estate & Location Strategy
- Alternate Data for Investments Houses
The Kogenta Grid is a global hex-based spatial system. Each hexagon is a stable geographic “unit of understanding” that allows all data layers to align perfectly – movement, demographics, surveys, POIs, indices and more.
Yes. The grid can be crosswalked into postal codes, ZIP codes, postcode sectors, local authority boundaries, or any custom geography required for reporting or planning.
Quality assurance is achieved through multi-source fusion, truth-set calibration, bias-correction routines, and AI models that detect anomalies or gaps. This creates stable and trusted nationwide data products.
- Busyness (hourly activity indices)
- Forecast Busyness (predictive activity modelling)
- Origin-Destination (AI-enhanced multimodal flows)
- SDK-generated ground-truth movement (opt-in device SDK)
It uses machine-learning models to learn historical patterns, fill in gaps, infer trends, weather forecasts and other known modifiers to predict likely activity at a precise hour-by-hour level for every hex in the grid.
Our OD model uses raw multi-modal, transport network data and AI modelling trained on actual transport journey truth sets to generate validated, multimodal travel flows – ideal for a wide range of uses requiring a clear, accurate picture of people movement.
The SDK captures high-accuracy, privacy-first movement behaviour from opted-in users, enabling public transport authorities to validate ridership, measure accessibility and refine planning.
Our Survey engine can run intercept, panel or location-based surveys and integrates the results as an additional Kogenta Connect layer – enriching movement and demographic insights with first-party context.
Multiple independent signal sources including transport and movement data, demographics, POIs, government datasets, SDK data, surveys, weather forecast and Kogenta’s proprietary contextual indices.
Kogenta operates under a strict privacy-by-design and privacy-by-default framework. In the vast majority of cases, we do not process any personal data. Where limited personal data may be involved, Kogenta acts strictly in accordance with the GDPR as a data processor or joint controller (as applicable), supported by comprehensive Data Processing Agreements (DPAs). All data is subject to rigorous minimisation, pseudonymisation or anonymisation measures, and processed solely for clearly defined, lawful purposes. Robust organisational and technical safeguards—including access controls, encryption, audit logging, and documented retention policies—ensure full compliance with GDPR principles throughout the data lifecycle.
These are proprietary Kogenta indices that quantify behavioural or locational characteristics relative to the national average – such as retail propensity, affluence, student concentration, leisure likelihood and other real-world dynamics.
Advertising agencies use Kogenta Connect as a core intelligence layer to build data-driven, evidence-backed pitches that win clients. The platform enables planners and strategists to precisely identify and size high-value audience segments, understand real-world behaviours within specific catchments, and surface the most impactful locations for OOH, DOOH, and cross-channel activation. Agencies use the granular H3-based insights to justify recommended media mixes, optimise postcode and micro-geography-level targeting, and demonstrate clear expected ROI before campaigns are launched.
Public transport authorities use Kogenta Connect to gain a detailed, evidence-based view of real-world mobility across their networks. The platform reveals demand patterns, the intent behind journeys, accessibility gaps, multi-modal flows, and latent ridership potential, helping agencies understand how people move and where services fall short. By combining AI-enhanced mobility layers, OD modelling and survey data, authorities can identify underserved corridors, first-/last-mile issues, and operational hotspots. Kogenta Connect also supports scenario modelling – allowing agencies to test service changes, evaluate new infrastructure, and validate business cases.
Cinema operators, Film Distributors and Cinema Advertising agencies use it to understand the characteristics of the local catchment area—identifying which audiences are likely to be attracted to each title and planning media spend accordingly.
Municipalities use Connect to understand community dynamics, plan infrastructure, support grant funding bids, manage tourism flows, evaluate new transport options and measure the performance of town centres.
CRE teams analyse footfall, demand, competitor locations, catchments, tenant mix performance and forecasting to support leasing, valuation and development decisions.
Retailers use Kogenta Connect to compare competitor locations, analyse shopping patterns, assess conversion potential, understand local demographics and optimise store portfolios.
Yes. Brands, retailers, transport providers, cinemas and agencies can overlay their first-party datasets -ticketing, transaction logs, loyalty, CRM, or sensor data – to enrich insights.
Connect supports API access, bulk downloads, dashboards, visualisation in Kogenta Explorer, and ingestion into BI platforms like Power BI, Tableau, Snowflake or Databricks.
Kogenta Explorer is an optional, add-on module of Kogenta Connect that is an intuitive web-based visualisation tool that allows clients to explore all Kogenta Connect layers, run reports, inspect geospatial patterns and generate stakeholder-ready maps.
Updates vary by layer, but movement and POI-based layers are frequently refreshed, demographic and underlying structural datasets are updated periodically, and live SDK streams are near-real-time.
It is designed for nationwide deployment at full-hex resolution. All data layers scale seamlessly across countries and can support billions of data points.
Most external datasets can be standardised, crosswalked to the grid, validated and added within days or weeks, depending on complexity and source format. Kogenta accelerates this process using AI-driven automation at each stage of the pipeline. Machine-Learning routines automatically detect schema structures, map fields to Kogenta’s unified data model, and identify the most appropriate crosswalk pathways into the H3 grid. AI-based anomaly detection flags inconsistent values, spatial distortions and temporal gaps, while automated imputation models repair or stabilise incomplete datasets. Combined, these automated steps significantly reduce manual engineering effort, improve data reliability, and allow Kogenta to bring new datasets online rapidly while maintaining strict quality, consistency and auditability standards.
Yes. Our predictive layers allow clients to test future scenarios, evaluate policy interventions, and forecast demand under different assumptions.
Yes. When combined with SDK data, sensor inputs, or telco integrations, it can support real-time or near-real-time dashboards and alerts.