Quick TopoJSON vector tile demo map, derived from an earlier GeoJSON demo. Original README follows.
A goofy slippy map of various vector tile data sources. With some fun colours, greetz to Aaron and Mike and Mike and the whole Prettymaps crew.
Python 2.7.6 (default, Dec 7 2013, 21:06:22) | |
[GCC 4.2.1 Compatible Apple LLVM 5.0 (clang-500.2.79)] on darwin | |
Type "help", "copyright", "credits" or "license" for more information. | |
>>> class MyClass(object): | |
... def __init__(self): | |
... self.foo = "foo" | |
... | |
>>> m = MyClass() | |
>>> m.foo | |
'foo' |
Quick TopoJSON vector tile demo map, derived from an earlier GeoJSON demo. Original README follows.
A goofy slippy map of various vector tile data sources. With some fun colours, greetz to Aaron and Mike and Mike and the whole Prettymaps crew.
"osm-processed_p1": { // Layer Name | |
// Sets ACCESS-CONTROL-ALLOW-ORIGIN header to "*" | |
"allowed origin": "*", | |
"provider": { | |
"class": "TileStache.Goodies.VecTiles:Provider", | |
// PostGIS Connection Info | |
"kwargs": { | |
"dbinfo": { | |
"host": "localhost", | |
"user": "matt", |
>>> import arcpy | |
Runtime error | |
Traceback (most recent call last): | |
File "<string>", line 1, in <module> | |
File "c:\program files (x86)\arcgis\desktop10.1\arcpy\arcpy\__init__.py", line 24, in <module> | |
from arcpy.toolbox import * | |
File "c:\program files (x86)\arcgis\desktop10.1\arcpy\arcpy\toolbox.py", line 342, in <module> | |
from management import Graph, GraphTemplate | |
File "c:\program files (x86)\arcgis\desktop10.1\arcpy\arcpy\management.py", line 22, in <module> | |
import _management |
This document is designed to present a basic overview of files contained within the larger WDNR Marine Spatial Planning (MSP) codebase, that were modified for the purposes of the WDNR Marine Vegetation Atlas (MVA). Additionally, newly created files, specific to the MVA, will be reviewed.
This visualization depicts a dynamic representation of stream flow in Cubic Feet per Second, "CFS", for three stream gauge stations in Washington State. A locally run python script is executed via Cron-job every hour, on the hour. The script is designed to query data from the USGS Instantaneous Values REST Web Service and format it for use in d3.
These data are then committed to github via a machine user account on the local machine. This visualization, stored as a gist, points to this periodically updated dataset.
Why do this? I wanted to learn the workflow for processing remote data on a local machine, with the end point of exposing it automatically via github. You could easily perform the data processing for this visualization using pure JS, but there are other use cases. What if, for example, you w
This simple bar chart is constructed from a TSV file storing the frequency of fart jokes that I've heard in my life, sorted by my age (Years). Note the emergence of mapfart by Aaron Racicot. The chart employs conventional margins and a number of D3 features: