Vector tiles for the Swiss Federal Geoportal

Geomatics Workbooks n° 12 – "FOSS4G Europe Como 2015"
Vector tiles for the Swiss Federal
Geoportal
Ingensand Jens, Marion Nappez1, Cédric Moullet, Loïc Gasser2 ,Sarah Composto1
1
GIS Lab, University of Applied Sciences Western Switzerland. Route de Cheseaux 1,
CH-1401 Yverdon-les-Bains
{jens.ingensand, marion.nappez, sarah.composto}@heig-vd.ch
2
Swiss Federal Office of Topography. Seftigenstrasse 264,
CH-3084 Wabern
{cedric.moullet, loic.gasser}@swisstopo.ch
Abstract
Vector tiling aims at cutting vector data into smaller entities. It has several
advantages for the development of web-mapping systems, such as the
possibilities to apply different styles, to access attributes or to render 3D data.
In this paper we investigate several parameters that need to be considered for
the generation of vector tiles. Furthermore we present several options that will
be tested in order to establish vector tile services for future versions of the
Swiss Federal Geoportal.
Keywords
Vector Tiling, Web GIS, WebMapping, Web Services, Geoportal, Vector Data
1 Introduction
The principle of vector tiling is similar to raster data tiling where large raster
data sets are tiled into smaller pieces and stored in hierarchical structures,
either in databases or in file systems. Raster tiling allows for an efficient
consumption of raster data through a network connection, for instance using
the standardized protocol WMTS (OpenGIS Web Map Tile Service,
www.opengeospatial.org) implementation standard.
Within the context of web-mapping systems vector data, as compared to raster
data, has several advantages – first, vector data allows for more flexibility for
rendering maps, such as the possibility to apply different styles or rendering 3D
data. Second, it is possible to transfer not only geometries, but also an entities
attributes. A third advantage is that, depending on the data layer, the features'
attributes, the feature density and the level of detail, data can be compressed
at a higher level. A fourth advantage is the fact that data can be reused and
transformed on the client side; for instance using coordinate transformation,
spatial analysis, and so forth.
However transferring vector data from a server also bears certain risks; e.g. the
fact that data can be illegally downloaded and reused for other uses that were
not intended by the administrators of a server infrastructure.
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The idea of tiled vector data services is to combine both the advantages of
vector data with the advantages of tiled raster data services.
Figure 1: Screenshot of the Swiss Federal Geoportal map.geo.admin.ch
Today the Swiss Federal Geoportal map.geo.admin.ch is the official geoportal of
the Swiss state, serving almost 400 data layers (see Figure 1) (Moullet 2015).
Map.geo.admin.ch is based on the open-source framework MapFish
(mapfish.org) which itself consists of several Python and Javascript libraries
such as Pylons (www.pylonsproject.org), OpenLayers (www.openlayers.org),
ExtJS (www.sencha.com/products/extjs) and GeoExt (geoext.org). The geoportal
uses Amazons EC2 and S3 (Amazon 2015) cloud computing infrastructure as a
service provider. A majority of the available data layers are generated using
WMTS web services; some layers are available as WMS (Web Map Service,
www.opengeospatial.org) services. About 2'500'000'000 WMTS unique raster
tiles are stored in the cloud infrastructure (Oesch 2014).
One problem in the context of vector tiling is the absence of standards. There
are standards for the transfer of vector data such as WFS (Web Feature Service,
www.opengeospatial.org) and standards for serving tiled raster data such as
WMTS, but today there is no standard for tiled vector data. Possible reasons for
this lack of standards might be found in the complexity of vector tiling.
Within the context of the Swiss Federal Geoportal, the goals of the project
GeoTile are:
• to investigate existing vector tile service solutions and to analyze which
are the different parameters that need to be taken into account for the
implementation of tiled vector services and for the development of
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•
•
clients that need to assemble data layers from cut vector data
to analyze which of the identified parameters and existing solutions
would be usable for the Swiss Federal Geoportal
to test different parameters in order to identify the best solution for tiled
vector services
In the following sections we will illustrate different solutions and concepts for
generating, serving and consuming vector tiles. Due to the vast scope of vector
tiling, only the most important approaches are cited. Thereafter we will discuss
which of these solutions will be tested in the Swiss Federal Geoportal
infrastructure in order to identify the solution that is best adapted for future
versions of the geoportal.
2 Different approaches to vector tiling
2.1 Regular versus irregular tiling
A first question in the context of vector tiling is whether a vector layer should
be cut into pieces of regular size (in terms of equally-sized and shaped
bounding-boxes, see Figure 2), whether a vector layer should be cut into pieces
with a similar payload or if each object (such a polygon or a point) should be
stored in a tile of its own (see Figure 2)
Figure 2: Examples for irregular and regular vector tiling.
Irregular tiling: each object is a tile of its own (left)
Regular tiling: a square bounding-box is applied to cut a layer into equallysized and shaped tiles (right)
Today several applications such as Google Maps (maps.google.com, Google Inc,
Mountain View (CA), USA), GISCloud (www.giscloud.com, London, UK) or
Polymaps (polymaps.org) have adopted the concept of regular vector tiling.
One notable example for irregular tiles has been described by Dufilie and
Grinstein (2014). In their approach shapes are cut using the TileSplit algorithm
into pieces of equal payload. The results are both an overview layer that
indicates the positions of the tiles and the tiles themselves. In order to
generate tiles for different levels of detail, the authors used Visvalingam's
algorithm (Visvlingam and Whyatt 1992).
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2.2 Formats for storing and transferring vector tiles
Similar to raster tiles, vector tiles can be stored in database structures or file
systems. Two common formats for transferring vector data in a web context are
XML-based formats such as GML and JSON-based formats such as GeoJSON and
TopoJSON. Both types of formats have advantages and disadvantages. XMLbased formats are extensible and offer several possibilities to verify data. The
disadvantage of XML-based formats is the fact that much storage is used for
tags. JSON files on the other hand need less space and JSON files are easily
parsed using Javascript (JSON 2015)
2.3 Generalization and simplification of vector data
Depending on the zoom level, data needs to be simplified and generalized in
order to maintain readability and payload. Several methods and algorithms
could be used within the context of tiled vector data.
One possibility to generalize vector data is the GAP (Generalized Area
Partitioning) tree method which focuses on topological area data. This method
identifies the importance of areas. Depending on the level of detail less
important areas are removed (Van Oosterom 1993). Tinghua and Van Oosterom
2002 have extended this method in order to include bridge areas between
aggregated polygons.
Another algorithm described by Visvlingam and Whyatt (2002) focuses on the
importance of a line's edges. In this method an edges neighbors are used to
calculate the area of a triangle. Depending of the calculated area, an edge is
considered more or less important. If the line needs to be simplified, less
important edges are removed. This method can be used both for linear features
as well as for area features.
The Douglas-Peucker (Douglas and Peucker1973) algorithm is perhaps the most
widely used algorithm for generalization in GIS software. The algorithm uses
distances between the points of a line to determine which points can be
omitted.
For point features, there are basically two possibilities to reduce the number of
points: Either points can be grouped into clusters, for instance using the KMeans clustering algorithm (MacQueen 1967), or thinned such as described by
for instance Moenning and Dodgson (2003). In both cases the loss of
information (e.g. in terms of attributes) is an important subject to consider.
2.4 Attribute handling
If geometric entities are cut into several pieces, one important question is how
attributes are handled. One solution to this problem is to replicate the
attributes to all pieces of the cut vector feature and thus to increase the overall
payload. Another possibility is to attach the attributes to one of the pieces and
to enable clients to reassemble geometries and attributes. A third solution is to
transfer attribute data separately and independently from the geometries.
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2.5 Client-side object assembly
A challenge in the context of vector tiling is the assembly of cut entities into
the original vector features. The main question is how a client can know that all
pieces of one entity have been transferred. The simplest method is to check all
tiles that have been received for e.g. the existence of pieces of one specific
entity. The drawback of this method is the time that is necessary to analyze all
tiles. Another method has been described by Nordan (2012) – in order to
locate pieces of tiled vector features, pointers can be included in the pieces,
indicating in which directions a client needs to look for more pieces.
3 Identification of the best approach
In order to find the method that is best adapted for the Swiss Federal
Geoportal, a testbed has been deployed. This testbed includes a copy of
several data layers stored in PostgreSQL/PostGIS databases. Python scripts will
be used to connect to these data sources and to produce vector tiles. The
following options have been chosen:
• Only tiles with equally shaped bounding-boxes will be generated (regular
tiling). The reasons for this decision are that the effort to generate
irregularly shaped tiles appears to be greater than for generating
regularly shaped tiles; second OpenLayers, the Javascript library that is
used in the geoportal already has a built in support for regularly shaped
raster-tiles (WMTS). Thereby an extension of this library (in order to
enable the consumption of vector tiles) would be easier to accomplish.
Furthermore an idea is to use the WMTS tile-indexation for the generation
of the tiles (e.g. a vector tile would be addressed using a similar URL as a
raster tile) to simplify the assembly of both raster- and vector tiles in a
client.
• Several algorithms for the generalization of vector tiles will be tested.
• All tiles at all levels of detail will be generated at once. Thereafter we will
analyze the payload, the stored features, etc. with statistical methods.
• Only JSON-based formats will be considered. The main reason is that
JSON-based formats are lighter than XML-based formats in terms of
payload and data processing.
• Attributes will be handled in separate files, independently from the vector
tiles. This has the advantage that no redundant data will be generated.
The main objective of our approach is to find a solution that balances flexibility,
performance and complexity. Important parameters that will be considered are
the payload of the generated tiles as well as the time it takes to generate the
tiles for a given layer. Moreover the generated tiles will be used as a repository
for the development of the Javascript client.
4 Discussion and conclusions
Vector tiling paired with webGL-enabled Javascript libraries permits to create
high-performance web-mapping applications. Today mostly proprietary
solutions such as Google Maps are using vector tiles. We can state that there is
a lack of standards for the generation and consumption of vector tiles. Vector
tiling however is a complex subject as there are many different possibilities to
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implement such services depending on the context of the application. In
Google Maps for instance there is a limited number of data layers available,
however these data layers cover the surface of the entire planet. The Swiss
Geoportal on the other hand offers hundreds of data layers – from point feature
layers containing all addresses in the whole of Switzerland to polygon layers
with all of Switzerland's buildings.
Our approach to tiled vector data refers to existing standards such as the
WMTS tile indexing and JSON-based file formats. Moreover we will use a
testbed that enables empirical testing of different alternatives.
Since this project is an ongoing project, at the time of writing no empirical
results are available.
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