poinproc marker clustering cartographic generalization

1. Marker clustering and cartographic generalization Berlin, november 2014.
About map scales
Classic cartographic generalization
Base maps are prepared to be represented at a certain
scale. Design scale and view scale must match.
Large scale: + detail. Small scale: - detail.
SSmmaallll ssccaallee vviieeww LLaarrggee ssccaallee vviieeww
Large scale base
Bad Well
Small scale base
Well Bad

2. Cartographic sources are drawn at a detailed scales. Then
they’re submitted to cartographic generalization to obtain
cartography at smaller scales.
3D feature
digitizing
environment for
ArcGIS from geo-referenced
aerial
or space-borne
imagery (Purview).
Marker clustering and cartographic generalization Berlin, november 2014.
About map scales
Classic cartographic generalization
Base maps are outlined from aerial photography.

3. Marker clustering and cartographic generalization Berlin, november 2014.
Base maps for web map viewers are prepared following the
same approach as classic (paper) map series.
About map scales
Classic cartographic generalization
Maps to be shown at different scales are drawn differently
Google Maps street map: its design is road map like.

4. Tools to obtain base maps
at less detailed scales:
cartographic generalization
tools.
Sometimes the application
will require some amount
of work.
Classification.
Simplification.
Marker clustering and cartographic generalization Berlin, november 2014.
About map scales
Classic cartographic generalization
Aggregation.
Collapsing.
Selection.

5. Marker clustering and cartographic generalization Berlin, november 2014.
But that’s not the case with Douglas-Peuker classic algorithm
to remove vertices automatically for line simplification, which
is fully automatic.
About map scales
Classic cartographic generalization
Douglas-Peuker method applied to polyline with a single line.
It is internally implemented in all internet map viewers.

6. Marker clustering and cartographic generalization Berlin, november 2014.
About internet mapping
Marker clustering
A set of markers is embedded into a vector layer. It can be
displayed as a map overlay for internet viewers. The viewer
handles scale changes in a way that we’re grown used to.
Some vector layer shown at two different zoom levels
We’re relying somewhat in the Maps API’s functionality
and somewhat in the web designer’s criteria to have a map
customized.

7. Marker clustering and cartographic generalization Berlin, november 2014.
This was Google Maps API’s incorporated marker clustering
procedure.
About internet mapping
Marker clustering
Pushpin markers and cluster markers behave differently under a click event
http://www.mapadacachaca.com.br/guia/

8. Marker clustering and cartographic generalization Berlin, november 2014.
Thematic maps are an elegant alternative approach. It is
being used a lot with cartographic data base servers
(Google’s Fusion Tables, etc.)
About internet mapping
Marker clustering
Thematic maps require polygon boundary layers and a data summary for each administrative entity.
http://www.cartovista.com/

9. Marker clustering and cartographic generalization Berlin, november 2014.
Another approach to overlay geometries to customize maps is
to convert zoom dependant vector overlays into tile layers for
web map servers.
About internet mapping
Marker clustering
Rasterizing vectors into tiles overcomes (maps APIs) restricitions to map different layers at different zoom levels.
http://www.skimap.org/

10. Marker clustering and cartographic generalization Berlin, november 2014.
PoinProc’s marker clustering service. It is a cartographic
generalization-like approach to swap sets of markers for
another zoom dependant geometries.
About internet mapping
Marker clustering
For low zoom levels, results are thematic map alike. Provides flexibility in click event handling.

11. Marker clustering and cartographic generalization Berlin, november 2014.
About PoinProc
Search by map
1 Use map controls
2 Select a place from a list
3 Preview location info 4 Select location and navigate

12. Marker clustering and cartographic generalization Berlin, november 2014.
PoinProc services
About PoinProc Additional services other than marker clustering.
Lines from points Paths (lines) clustering Orthodromics generalization

13. Marker clustering and cartographic generalization Berlin, november 2014.
How to use PoinProc: web
You can use the internet service by filling the convenient web
form. A data table is required in csv, tab, text format.
About PoinProc
Latitude and longitude are required. It is desirable to give a name to each
marker, as well as geographical entities names to summarize and separate.

14. Marker clustering and cartographic generalization Berlin, november 2014.
How to use PoinProc: desktop
The desktop application is under development.
About PoinProc
Campos latitud, longitud

15. Marker clustering and cartographic generalization Berlin, november 2014.
Javascript API
Scripts for Web maps APIs: easily loading different geometries
for each zoom level.
var mapViewer = new ZoomDepViewer (mapdivID, MapClick, LoadIcons, getColour);
function MapClick(descrip) {
var expla = document.getElementById('expla');
var descripItems = descripTxt.split('.');
var descripHtml = descripItems.join('<br/>');
expla.innerHTML = descripHtml;
}
var LoadIcons = function(viewer) {
var iconfolder = "www.poinproc.com/Schemas/";
var icons;
icons[0] = { icon: iconfolder+'point0.png',height:15,width:15 };
icons[1] = { icon: iconfolder+'point1.png',height:22,width:22 };
icons[2] = { icon: iconfolder+'point2.png',height:32,width:32 };
return icons:
}
function getColour(code) {
var fillColor = ["#963296", "#C86432", "#329696", "#48C848", "#6432C8"];
var index = code % 5;
return fillColor[index];
}
mapViewer.showXMLData(xmlData, viewer, useIDs); // viewer :'gmapsv3', 'bing', 'ol'
About PoinProc