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change of the density

construction activity

moving there/ away

ratios of the different job sectors

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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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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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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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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)

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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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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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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

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

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

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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.

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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

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CORINE Land Cover is a pan-European land cover inventory with 44 thematic classes. Initiated in 1985 (the 1990 reference year) the inventory is available for the 1990, 2000, 2006, 2012 and 2018 reference years including change layers 1990-2000, 2006-2012 and 2012-2018.

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Elevation data

Less population in hilly areas

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Additional data helpibg to refine choropleth map data

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Shows us the true picture with different factors involved

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Shows us the true picture with different factors involved

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not political but rather data-related boundaries

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

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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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agriculture happens more on cropland and less in urban areas

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

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Because we add additional information leading to a more realistic visualization

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Because we add additional information leading to a more realistic visualization

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Limiting and/or related variables

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

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

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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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less agriculture the higher we are

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

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

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

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in ancillary databases

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Usually not complementary but either-or

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map.geo.admin/Swisstopo

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On ESRI or other sites with satellite and land cover data

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Satellite imagery

Land cover/land use datasets