Hello
I am seeking help and advice for a project started by me as a noob. I want to create a network that consists of roughly 200,000 geo-located tiles (the tiles cover the entirety of the Germany around 1937 and reperesent density of population) which are being related to places of slave labor, concentration camps and deportation train routes. The basic idea is to visualise, how close or distant civilian life was going on in the nearby of the slave labor and extermination camp system and estimate possible spreading of knowledge about it. So far I have a geojson file (converted to csv and json also) that has such a tile-collection following the basic logic of:
| Region Type | Tile Size | Example Locations | Reasoning |
|---|---|---|---|
| Dense Urban Centers | 100m × 100m | Berlin, Hamburg, Munich | High population, potential witness density, increased movement |
| Urban Areas | 250m × 250m | Leipzig, Nuremberg, Cologne | Moderate visibility, significant civilian activity |
| Suburban / Towns | 500m × 500m | Regional towns, mid-sized cities | Balance between detail and computational efficiency |
| Rural Areas | 1km × 1km | Farmland, villages | Lower population density, fewer events directly witnessed |
| Sparse Regions | 5km × 5km | Forests, uninhabited areas | Very limited visibility impact, reduced relevance |
Now I'd like to use these locations as nodes and visualise them by layering them on a map. Then I'd like to produce another set of nodes that represent all the camps and then relate the tiles to the camp node with edges defining likelyhood of having noticed these events or locations (edge kind defined by basic qualities such as visible, hearable, known by rumor and so on and these kinds then wheighed; a tile can accumulate potential knowledge, which it constantly disperses to adjacent tiles; overall knowledge constantly diminishes gradually (knowing individuals die or forget). Can anyone kindly point me to where I could find help with this idea?
(not sure how to share the csv here properly, wfiw here's a limited wetransfer link: https://we.tl/t-NaewOvgutD )
Kind regards
Fabian