"Herodot" - applied predictive location intelligence

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"Herodot" - applied predictive location intelligence by Mind Map: "Herodot" - applied predictive  location  intelligence

1. 1. Proposition

1.1. a. Vision & Value

1.2. a. Data Sources

1.2.1. Personal Mobile devices (Tef NEXT/LUCA)

1.2.2. Static geodata

1.2.2.1. Local Commerce

1.2.2.1.1. Property values (Europace, Mariane Della Rocca)

1.2.3. Public Transport (Siemens HaCon)

1.2.4. Google Maps Apic

1.2.5. Street sensor data

1.2.6. GPS data

1.2.6.1. Locarta

1.2.6.2. Cars (HERE)

1.2.6.3. Wetter.com

1.2.6.4. INRIX

1.3. c. use cases

1.3.1. B2B use cases

1.3.1.1. a. Crowd Alert (Alert)

1.3.1.1.1. Demo für Berliner Polizei

1.3.1.1.2. Sicherheit für Bahnsteige/Massenveranstaltungen

1.3.1.1.3. Andrang auf tourist. Attraktionen/Outlet Promotions

1.3.1.2. b. Regional traffic/movement cockpit

1.3.1.2.1. Überblick/Infrastrukuturplanung

1.3.1.2.2. Prognose/Alerting

1.3.1.2.3. (perspektivisch Verkehrslenkung)

1.3.1.3. c. Long term trends (e.g. location/schools

1.3.2. End-user-benefits

2. 2. Clients & partners

2.1. a. Potential clients

2.1.1. Public

2.1.1.1. Landrat/Metropole Rhein Neckar (Christian)

2.1.1.2. Bürrgermeister Stadt (z.B. Ode)

2.1.1.3. Verkehrssenat Berlin

3. 3. Business plan

3.1. Country scope

4. 6. MVP / NEXT Steps

4.1. a. Scope

4.2. b. To do's

5. 5. Admin / Company Set-up

5.1. a. Tech stack

5.1.1. PTV Vizum

6. 5. Ecosystem & Partners

6.1. Startups

6.1.1. Geoalert

6.1.2. Geopsin

6.1.3. Traak Systems

6.2. Potential Mentors, Sponsors& Coaches

6.2.1. Georg Bauer, Chairman Fair.com

6.2.2. Martin Koers, Geschäftsführer VDA

6.2.3. Stefan Gilmozzi. Ex-founder Viveon

6.2.4. Maxmilian Schaefer, Instafreight