GEO123 Lecture 13 :: Dasymetric Mapping Questions by fabs

CROPLAND🦅🦅🦅🦅

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Satellite imagery

Land cover/land use datasets

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On ESRI or other sites with satellite and land cover data

jkp2sc7ygf — 2026-05-19 09:48:11 UTC

map.geo.admin/Swisstopo

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Usually not complementary but either-or

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in ancillary databases

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Above a certain elevation, agriculture is not possible anymore (e.g. >900 MasL)

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At certain elevations agriculture is not possible anymore = needs to be incorporated when mapping and analyzing

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Land cover data would be useful because there are probably not many people living in the woods :)

2026-05-19 09:49:04 UTC

less agriculture the higher we are

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With higher elevation crop production becomes smaller, so it makes sense to have a cur off at a certain elevation

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More specific in where the objects/data accumulates, very local rather than just the entire area

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its more focused and more exact, specyfing the areas where actual farming happens

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Limiting and/or related variables

Because we add additional information leading to a more realistic visualization

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No agriculture on certain surfaces, e.g ice, mountains, water bodies…

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agriculture happens more on cropland and less in urban areas

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There are land cover types like rock and ice where there is zero crop production but there are also types like woodland where while not being designated cropland still has some production

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Certain land cover types do or don‘t allow for agricultural use

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not political but rather data-related boundaries

Shows us the true picture with different factors involved

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Additional data helpibg to refine choropleth map data

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Elevation data

Less population in hilly areas

Data sources used

fabs — 2026-05-19 10:47:32 UTC

For the agricultural production example in class we used

Corine Landcover/landuse data from the EU - see link above.

For Switzerland you could also use

Swiss landcover/landuse data : https://www.bfs.admin.ch/bfs/en/home/statistics/territory-environment/land-use-cover.html

fabs — 2026-05-19 12:02:23 UTC

Agricultural production is how the land is used -> landuse. Urban landcover is what exists on the Earth's surface. Depending on the landcover, agricultural use/production might be more better or worse.

fabs — 2026-05-19 12:04:58 UTC

For the calculation example we used cropland with JK Wright's formula.

For the Swiss case study we used agricultural production across Swiss Cantons.

fabs — 2026-05-19 12:09:57 UTC

For the Swiss case study, we used is

we used Corine landcover data

limiting variables:

urban, water, ice, rock land cover data

related variables:

agricultural, pasture, mixed, forest landuse/landcover data

fabs — 2026-05-19 12:18:19 UTC

The dasymetric refinement reallocates the density of the mapped phenomenon within political (i.e., enumeration) boundaries (choropleth map) using additional data to create more realistic data-driven boundaries (dasymetric map).

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Additional information about the area/phenomenon which will be used as limiting/ related variables. In our example we've used elevation and land covers as limiting factors and further land cover types as related variables to further distinguish the agricultural production.

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A rather negative relationships since in urban areas agricultural production isn't possible. It could be possible that right around bigg(er) urban areas agricultural land is less likely to be found (agglomeration, streets, train tracks etc. that condense around bigger urban areas)

2026-05-21 12:43:25 UTC

The higher, the less crop production. Besides changing conditions like rain fall and oxygen levels that make it more difficult to grow crop in higher altitudes, the methods to grow crops get more complicated and strainous. Thus, probably also more expensive.

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Agricultural production can happen in/between other (similar) land cover types. But at the same time other types can make it impossible for the same relationship to happen. Certain anthropogenic usage of land types have influences on agriculture, like the density of roads around urban areas, construction etc.

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It's a refinement in the sense of accuracy and consideration of user group of the map. In the final map one learns more information, e.x. distinguishing between different kinds of agricultural production and even seeing that there are different kinds. Because of that we were able to interpret the locations of vineyards due to the refined map in class.

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change of the density
- construction activity
- moving there/ away
ratios of the different job sectors