The last couple of weeks i have been working on a raster to vector conversion in FME, and thought it would be nice to share my experiences here at my blog. This might be the start to a FME special series here at the blog with short explanations of different topics within FME. To read this raster to vector special click the read more link below.
What i wanted to do was to create polygons representing each of the rastercells, and extract the value from the raster and copy this value to the attribute table of the polygon.
I started off importing the raster to my workspace. One could also do this as a batch operation, importing multiple rasters.
To convert the raster cell to polygon i used the the ‘RasterCellCoercer’. This transformer decomposes each of the raster features into individual points or polygons. However, the transformer does not extract the value, even when enabling the “preserve attributes” option.
To be able to extract the value from each of the raster cells into an attribute i used the transformer ‘ElevationExtractor’. This tool extracts the z-value from each of the raster cells into a defined attribute. The default is “_elevation”.
That is basically it. You can now add your output writer and add the user attribute you want to store the raster cell value from the “_elevation” attribute. Remember that your output feature must be equal to either the point or polygon option that you selected in the ‘RasterCellCoercer’.
If you have selected multiple rasters, you could write each out to its original filename using fanout by “fme_basename”.
Here is my workspace:
A new report from CIESIN and the Austrian Insitute of Technology has released an global assessment on light pollution on protected areas.
The analysis is done to reveal how light pollution affects protected areas. Hence, two new variables was created from combining the global protected area distribution data and nighttime lights data: a Protected
Area Light Pollution Index (PALI) and a Protected Area Human Impact Index (PAHI).
Results indicate that regions in Europe and Asia Minor, the Caribbean, South and East Asia as well as in
the Eastern part of the United States are most affected. Introducing aggregated data on biomes reveals that
temperate broadleaf and mixed forests suffer the biggest impact both in terms of general light pollution as
well as lighting in protected areas. The presented risk assessment underscores the need for accurate and
consistent spatial data on a global scale and can help to indicate which protected areas globally and
nationally are at greatest risk of human activities. It is also an important step towards public communication
and raising general awareness on the topic of light pollution.
Aubrecht et al. (2010): GLOBAL ASSESSMENT OF LIGHT POLLUTION IMPACT
ON PROTECTED AREAS.
Read the whole report here:
CIESIN and Yale University has released the 2010 data of their data set on environmental performance index.
GisIntersect has created a map from the dataset, available here
Click the map for full size:
SEDAC at CIESIN has released a interactive mapper that visualizes the Human Influence and the Human Footprint dataset (version 2). The data has a global coverage and is available in 30 arc seconds grid cells and 1 km grid cells.
Not much area that can be defined as untouched by humans anymore!
The service is built up with Open Layers on Geoserver.
Globally consistent spatial road data sets are indeed scarce. Many of the available data sets has data with highly variable consistency.
A new project hosted at CIESIN is due to release a new globally consistent road data set named gRoads.
For road density mapping, this will provide a good alternative. However, the data is not meant to cover street data, but roads between settlements.
The project is due to release in early 2010, which is hopefully about now.
More information here.
Nils Weidmann at Princeton University and Kristian Skrede Gleditsch at the University of Essex has constructed this historical border dataset. The cShapes package includes all state border changes from 1946 and onward. It provides an excellent dataset to visualize border changes, in addition to construct historical data. The dataset is available both as an R package, and shapefile format.
See Nils Weidmann’s webpage for more information
ArcGIS online resources center provides some very interesting basemaps for free. If you only need some background maps to visualize your information on, then this is a good solution.
The service includes both basemaps, reference maps and elevation maps.
There is a high degree of US maps, but still lots of valuable and time saving maps for the rest of us.
More info here.
The Geospatial Modelling Environment (GME) is the predecessor of the Hawths Tools extension for ArcGIS. It is designed to facilitate rigorous spatial analysis and modelling by linking ArcGIS to the statistical software package R.
The new software has increased graphic ability in addition to batch processing, and records the workflow to ease replication.
One additional huge benefit with this new release is the ability to call on GME commands through Python.
More info here.
See also the documentation here.
FME or “Feature Manipulation Engine”, is a spatial ETL (extract, transform and load) engine, that supports over 250 formats. The new 2010 desktop version of the software includes new powerful transformers, which are able to do various geoprocess your data.
The neat part with FME is the ability to make complex models (something like ArcGIS model builder), and the ability to translate almost any spatial data format.
More information available here
According to crisismappers.net, the 2010 conference will be held in Boston on October 1-3. The website for the conference is due to be published february 15.
See ICCM 2010 for more information.