Generalisation process and generalisation tools in Maanmittauslaitos Ari Öysti Small scale map production 2.2.2017

Automated generalisation process Operator starts the batch process Data flows from tool to tool in automated batch process. Input data Generalizedoutput data Tool 1 Tool 2 Tool 3 Is this really possible in complicated generalisation process ? Yes it is possible: e.g. fully automated generalisation process in Holland Kadaster 1:1000 -> 1:50 000

Interactive generalisation process Data flows from tool to tool controlled by operator. Operator checks the validity of the input data and output data in every phase of the process. Tools can’t do everything. You need also manual interactive work during the process e.g checkings and corrections. We use this kind of process in our generalisation process. Tool 2 Generalizedoutput data Input Data Tool 1 Tool 3 Operator starts tool Operator starts tool Operator starts tool Operator starts tool

Tools are not enough, you need to desing the whole generalisation process = generalisation process model What generalize operations = tools are used for each map theme. First thing is to define the generalized output product. In what order map themes are generalized. What controll parameters are used for each operations = tools for each map theme. Does the different map themes have topological relationships which should be maintained during the process. Is it necessary to spend too much time and money for producing over-quality in small details if enough-good product is ok for the users. Small scale maps are usually used as zoom-in and zoom- out maps.

Dataset scales Cartographic Names Topographic layers Topographic DataBase 1:10 000 Name DataBase 1:25 000, 1:50 000 JAKOmtj Smallworld Map 1:25 000 Map 1:50 000 Transfer from Smallworld to ArcGis in shape format Generalisation Manual generalisation 1:100 000 2006-2010 1:100 000 2008 International datasets Generalisation Manual generalisation ERM, EBM 1:250 000 2009-2010 1:250 000 2008 Generalisation Manual generalisation 1:500 000, 1:1 000 000 2011 EGM 1:1 000 000 2011 Manual generalisation Generalisation 1:2 000 000, 1:4 500 000, 1:8 000 000 1:4 500 000 2011 2011 ArcGis/Geodatabase JAKOmtj/Smallworld

Map theme layers are generalized separately in own generalize processes. Topological relationships between themes are maintained. Hydrography theme Rivers Land use and vegetation theme Lakes Agricultural areas Sea Wetlands Transportation theme Rocks Roads Administration theme Railways Boundaries Airports Areas Settlements theme Elevation theme Buildings Contour lines Built-up-areas Cartographic names theme

Generalize processing is done with 5 PC’s by 5 persons in Pasila and 1 person controlling the workflow Data is divided into working areas and working areas are processed at workstations. After processing these working areas are merged and edgematched to seamless dataset. 1:10 000 (vector format 80 Gb) 1:100 000 (vector format 3 Gb) 1:250 000 (vector format 600 Mb) 1:1 000 000 (vector format 80 Mb) 1:4 500 000 (vector format 5 Mb) e.g. Transportation theme from 1:10 000 -> 1:100 000 takes about 6 months and 1:100 000 -> 1:250 000 about 3 months Processing 1:1 000 000 and 1:4 500 000 datasets is done whole country as one working area

Main use for generalized maps are zoom-in and zoom-out orientation rastermaps in our own mapservices. This is probably the main use how the customers use these datasets. We don’t make printed maps from these generalized datasets

Generalized datasets and our own rastermap products in our mapservices Topographic map 1:100 000 Background map 1:40 000

Generalized datasets and our own rastermap products in our mapservices Topographic map 1:250 000 Background map 1:160 000 Background map 1:80 000

Generalized datasets and our own rastermap products in our mapservices Topographic map 1:1 000 000 Topographic map 1:500 000 Background map 1:320 000 Background map 1:800 000

Generalized datasets and our own rastermap products in our mapservices Topographic map 1:2 000 000 Topographic map 1:4 500 000 Topographic map 1:8 000 000

Generalized datasets and our own rastermap products in our mapservices Background map 1:2 000 000 Background map 1:4 500 000 Background map 1:8 000 000

Own generalisation tools = PIEKKA application PIEKKA = PIEnimittaKaavaiset KArtat ESRI Finland’s GIS-Award of the Year 2011

PIEKKA application Developing of the application started 2003 (3 developers, 2 test people) New generalisation production system in use since 2006. Basic idea for generalisation process: Avoid interactive generalisation work and use automated tools when it is possible Tools in standard GIS-applications are too simple for high quality generalisation Developing own generalisation tools was a big challenge. Based on ESRI ArcGis application and ArcObjects componets ESRI’s ArcObjects components provides a wide component library for developing own generalisation tools Own tools are integrated into ESRI’s standard ArcMap application using ArcGis addinn setup file Database format is ESRI ArcGis FileGeodatabase format Topological relationships between features are maintained in FileGeodatabase with ArcGis topology tools

PIEKKA application PIEKKA generalisation tools are integrated to ArcMap application

Generalisation parameters PIEKKA application Generalisation parameters All parameters for controlling generalisation process are stored into separate control database tables. Application reads the generalisation parameters from the tables of the control database. All data is generalized in the same way and users can’t use wrong parameter values by mistake. You don’t have to rewrite the tool’s code if you want to change the parameters. Same tools can be used for generalisation of different scales. Typical cartographic visualisation and symbolisation rules in each map scale defines the values of generalisation parameters.

Generalisation parameters PIEKKA application Generalisation parameters Same tools can be used for different levels of generalisation by switching the generalisation parameter database.

Generalisation parameters PIEKKA application Generalisation parameters Same tools can be used for different levels of generalisation by switching the generalisation parameter database.

Generalisation parameters PIEKKA application Generalisation parameters For special cases user can set generalisation parameters by using tool’s user dialog forms

Generalisation methods PIEKKA application Generalisation methods Generalisation operations available with customized tools: Simplification Collapsing Enhancement Selection Elimination Displacement Aggregation / Merging Generalisation application has 35 tools to run generalisation operations listed above. Usually tool for running one operation consists of several base methods

Base methods for polygon aggregation operation Piekka application Base methods for polygon aggregation operation Narrow sections Create merging area between polygons Create centerline into merging area Clip the merging area by using the centerline and join together the clipped parts into neighbor polygons

PIEKKA application Examples Selection by attributes of features by geometry (length or area size) Feature type collapse polygon  line polygon  point line  point

PIEKKA application Examples Line and Area simplification by Douglas-Peucker algorithm

PIEKKA application Examples Type collapse: Polygon -> Line Narrow polygon river -> line river

PIEKKA application Examples Displacement of buildings lying near the road line Typification of dense building clusters (= maintain the general pattern of building group shown in their approximate locations)

PIEKKA application Examples Buildings are not moved over water areas or other side of the road or railway or river.

Line generalisation PIEKKA application Examples Line fusion Line merge Line sections under minimum length are joined together with the longest neighbor. Line merge Separate lines near each other are replaced by creating a centerline between original lines and transfering attributes to new line Removing under minimum length branch lines If there are several under minimum length branch lines at the same node the longest one is kept and the other branch lines are removed

PIEKKA application Examples Merging and removing rules for small area features with different land use classes.

PIEKKA application Examples

PIEKKA application Examples Rules for merging and simplifying area features

PIEKKA application One of the most challenging task for automated generalisation = complicated road junction 1:10 000 1:100 000 We do this manually!

Thank You ari.oysti@maanmittauslaitos.fi