Data Visualization Software | Tulip FMEpedia - Reading Complex XML or GML using the XMLFlattener Many users have problems reading complex XML or GML. In the past you had to make xfMap or XQuery scripts. To address this we recently we added the concept of XML flattening to our XML and GML readers. However, sometimes you have XML elements or attributes that need to be processed within a workspace, such as the result from a URL request. With XMLFlattener, all you have to do is feed it XML on an attribute or from a file, specify the node you want to query, and XMLFlattener transformer will make an FME feature for each occurrence of that node in your XML and flatten all the elements nested within that node into simple FME attributes. Note that the same approach can now be used within the standard FME XML reader by using the reader parameter confguration type = 'Feature Paths' and flatten options set to enable flattening. Note** This example requires FME 2012+ So, given the following input, xml_string = to: <? Remember you can always have as many queries as you want. 1. e=1 e=2
GRASS GIS manual: v.lidar.edgedetection v.lidar.edgedetection - Detects the object's edges from a LIDAR data set. vector, LIDAR, edges v.lidar.edgedetectionv.lidar.edgedetection helpv.lidar.edgedetection [-e] input=name output=name [see=float] [sen=float] [lambda_g=float] [tgh=float] [tgl=float] [theta_g=float] [lambda_r=float] [--overwrite] [--verbose] [--quiet] Flags: -e Estimate point density and distance Estimate point density and distance for the input vector points within the current region extends and quit --overwrite Allow output files to overwrite existing files --verbose Verbose module output --quiet Quiet module output Parameters: input=name Name of input vector map output=name Name for output vector map see=float Interpolation spline step value in east direction Default: 4 sen=float Interpolation spline step value in north direction lambda_g=float Regularization weight in gradient evaluation Default: 0.01 tgh=float High gradient threshold for edge classification Default: 6 tgl=float Low gradient threshold for edge classification Default: 3
Kosmo-Plataforma SIG libre corporativa - Home Open Source Tool Sets for Creating High-density Maps A few weeks ago I wrote about some of the building blocks available to create engaging maps in Drupal . The module ecosystem around maps and other geospatial functionality is pretty flexible, but there are some situations where Drupal might not be the best fit. There are some great tools out there to build out sites when the situation calls for little else besides a map display, or when processing and rendering tens of thousands of geospatial features. In this blog post, I’m going to talk about two in particular, Mapbox and CartoDB . Mapbox Mapbox is a great map building tool that allows for the creation of pre-rendered mapping displays with a set of web-friendly tools. Styling maps in Tilemill is handled through a language called CartoCSS , which shares many syntax similarities to CSS and Sass. CartoDB CartoDB is a mapping stack that has many similarities to Mapbox. Like anything in life, there are tradeoffs. Real-word Mapping Example: Finding Apartments in NYC Step 1. Step 2. Step 3.
GME | SpatialEcology.Com Copyright (c) 2009-2012 Spatial Ecology LLC Before proceeding with the installation and/or use of this software please read the following terms and conditions of this license agreement. The license agreement applies to both the GME interface and the installation program. By installing or using this software you indicate your acceptance of this agreement. If you do not accept or agree with these terms, you may not download, install or use it. Permission is hereby granted, free of charge, to use this software and associated documentation files (the "Software") subject to the terms of this EULA. You, the user, are responsible for ensuring that the Software is used appropriately, and that the output of this Software is accurate, relevant, consistent, and otherwise error-free. You are free to copy and redistribute this software within your own organization.
FMEpedia - SherbendGeneralizer Examples SherbendGeneralizer is an FME 2011 transformer for 'smarter' generalization, which preserves original topology of the features. The goal of the Sherbend algorithm is to reduce unnecessary details on a line based on the analysis of the line’s bends. Sherbend is a constraint based algorithm that preserves topology of the lines and points in the input dataset. The Sherbend algorithm iteratively generalizes bends in a line by using the diameter parameter to select bends for generalization. The strategy for generalizing bends in a line looks as follows: * The parameter "diameter" is used to calculate the area of a reference circle. * For each line, determine the locations of the bends. * For each bend, calculate its actual area. * For each bend, calculate its circumference. Example 1: Topological CheckingBack to Top by Dmitri Bagh Often it is better to see a simple demo, than to read a long detailed explanation. Old Style Before Generalization It can control: 1) Self-intersections; Sherbend Style
A Beginner’s Guide to pgRouting Please read the new instructions for pgRouting 2.0. The aim of this post is to describe the steps necessary to calculate routes with pgRouting. In the end, we’ll visualize the results in QGIS. This guide assumes that you have the following installed and running: Postgres with PostGIS and pgAdminQGIS with PostGIS Manager and RT Sql Layer plugins Installing pgRouting pgRouting can be downloaded from www.pgrouting.org. Building from source is covered by pgRouting documentation. Start pgAdmin and create a new database based on your PostGIS template. Creating a routable road network The following description is based on the free road network published by National Land Survey of Finland (NLS) (Update January 2013: Sorry, this dataset has been removed). First step is to load roads.shp into PostGIS. pgRouting requires each road entry to have a start and an end node id. Next, we create start and end point geometries. (This can take a while.) I recommend adding a spatial index to the resulting table.
OpenJUMP GIS Gephi makes graphs handly Geospatial Analysis - spatial and GIS analysis techniques and GIS software The FME Evangelist » FME2011 Use Case: Joiner vs FeatureMerger Hi FME’ers, Interacting with FME users I see various points of view on the merits of the Joiner transformer versus the FeatureMerger. Although both transformers carry out similar actions, it’s not clear to users when you should use each of these – particularly in relation to workspace performance. So this post will indulge in some investigative journalism! I’ll compare and contrast these transformers, to see where you would want to use each of them, and throw other transformers – such as the updated SQLExecutor and the new FeatureReader - into the mix as potential alternatives. Descriptions First a description. To do the merge requires some common information; usually a common ID number. FeatureMerger The FeatureMerger is for when both sets of data are being read in a workspace. Joiner The Joiner has only a single input port. For format, the spatial data was written to Shape, but the non-spatial to both CSV and SQLServer. So here the Joiner wins out: it’s quicker and uses far less memory.
GFOSS - Free Software GIS at your fingertips