The whole infrastructure visualised,
including the rate of update of the infrastructure.
Or temporal and spatial network analyses.
You can even use this qualitative analysis for inter-network analysis
I have even live details for you :)
00 Coordinate reference systems59 organisations, 134 datasets, 293 relations |
01 Geographical grid systems52 organisations, 141 datasets, 271 relations |
02 Geographical names1681 organisations, 5464 datasets, 12277 relations |
03 Administrative units398 organisations, 1910 datasets, 3614 relations |
04 Addresses1660 organisations, 2157 datasets, 5973 relations |
05 Cadastral parcels222 organisations, 3437 datasets, 5977 relations |
06 Transport networks692 organisations, 2697 datasets, 5412 relations |
07 Hydrography440 organisations, 1952 datasets, 3860 relations |
08 protected sites717 organisations, 1971 datasets, 4745 relations |
09 elevations344 organisations, 5595 datasets, 15476 relations |
10 Land cover492 organisations, 2021 datasets, 4582 relations |
11 Orthoimagery231 organisations, 1098 datasets, 2462 relations |
12 Geology226 organisations, 1425 datasets, 3355 relations |
13 Statistical units146 organisations, 484 datasets, 1149 relations |
14 Buildings306 organisations, 1117 datasets, 2199 relations |
15 Soil237 organisations, 790 datasets, 1811 relations |
16 Land use2544 organisations, 44294 datasets, 96357 relations |
17 Human health and safety383 organisations, 1065 datasets, 2504 relations |
18 Utility and governmental services689 organisations, 2420 datasets, 5342 relations |
19 Environmental monitoring facilities460 organisations, 1394 datasets, 3481 relations |
20 Production and industrial facilities262 organisations, 605 datasets, 1485 relations |
21 Agricultural and aquaculture facilities133 organisations, 338 datasets, 739 relations |
22 Population distribution demography50 organisations, 167 datasets, 334 relations |
23 Area management, restriction, regulation zones and reporting units1580 organisations, 12883 datasets, 27992 relations |
24 Natural risk zones339 organisations, 2581 datasets, 5639 relations |
25 Atmospheric conditions87 organisations, 212 datasets, 588 relations |
26 Meteorological geographical features56 organisations, 179 datasets, 438 relations |
27 Oceanographic geographical features65 organisations, 521 datasets, 2873 relations |
28 Sea regions50 organisations, 285 datasets, 654 relations |
29 Bio-geographical regions111 organisations, 149 datasets, 426 relations |
30 Habitats and biotopes403 organisations, 946 datasets, 2575 relations |
31 Species distribution260 organisations, 653 datasets, 1918 relations |
32 Energy resources161 organisations, 284 datasets, 741 relations |
33 Mineral resources93 organisations, 171 datasets, 435 relations |
How many datasets does every MS publish under every INSPIRE theme
How populated is every INSPIRE theme
How many MSs do not provide any data under specific INSPIRE themes
How certain MSs provide detailed source data instead of national datasets
How impossible is to identify national datasets from metadata or inter-relations between the datasets but how national data hubs pop up even from highly complicated structures
How certain themes contain highly intertwined relations with several organisations contributing (even cross border) to the final product (e.g. land cover)
How complicated the regionalization of some MSs is. Please see transport data to see FR/ES/DE reflection of the regional structures
How often does a dataset has different provider and publisher (and sometimes even more), you can spot it when a pink dot connects to more than one green dot.
How much more effort is needed yet to be invested in a good metadata methodology how to apply the standard consistently across the MS
