| Survey Selected: brown_island_sja_nelson_1997 | |
| Logfile Location: csv_logtest.csv | |
| Source Geodatabase: E:\Temp\WDNR_CitrixWorking\Archive\MVA_Catalog_ks_2.13.mdb | |
| Feature Class: E:\Temp\WDNR_CitrixWorking\Archive\MVA_Catalog_ks_2.13.mdb\SurveyPoint | |
| Log Generated: 20130307_0925 | |
| TABLE: SurveyPoint | |
| SurveyID,SurveyNM,FullCit,StartDT,EndDT,SurveyZone,SurveyFREQ,DataScale,has_Map,StudyID,is_ExtDel,DataConID,Shape | |
| brown_island_sja_nelson_1997,Interannual variance in a subtidal eelgrass community,"Nelson, T. A. 1997. Interannual variance in a subtidal eelgrass community. Aquatic Botany 56: 245-252.","WARNING: ""StartDT"" Missing Value.",1992-07-30 00:00:00,sub,"WARNING: ""SurveyFREQ"" Missing Value.",site,No,brown_island_sja_nelson_et_al_1997,No,nelson_t_spu,<geoprocessing describe geometry object object at 0x08120BE0> |
| Survey Selected: brown_island_sja_nelson_1997 | |
| Logfile Location: csv_logtest.csv | |
| Source Geodatabase: E:\Temp\WDNR_CitrixWorking\Archive\MVA_Catalog_ks_2.13.mdb | |
| Feature Class: E:\Temp\WDNR_CitrixWorking\Archive\MVA_Catalog_ks_2.13.mdb\SurveyPoint | |
| Log Generated: 20130307_0925 | |
| TABLE: SurveyPoint | |
| SurveyID,SurveyNM,FullCit,StartDT,EndDT,SurveyZone,SurveyFREQ,DataScale,has_Map,StudyID,is_ExtDel,DataConID,Shape | |
| brown_island_sja_nelson_1997,Interannual variance in a subtidal eelgrass community,"Nelson, T. A. 1997. Interannual variance in a subtidal eelgrass community. Aquatic Botany 56: 245-252.","WARNING: ""StartDT"" Missing Value.",1992-07-30 00:00:00,sub,"WARNING: ""SurveyFREQ"" Missing Value.",site,No,brown_island_sja_nelson_et_al_1997,No,nelson_t_spu,<geoprocessing describe geometry object object at 0x08120BE0> |
| <!DOCTYPE html> | |
| <html> | |
| <head> | |
| <meta http-equiv="Content-Type" content="text/html; charset=utf-8"> | |
| <meta http-equiv="X-UA-Compatible" content="IE=7,IE=9"> | |
| <!--The viewport meta tag is used to improve the presentation and behavior of the samples | |
| on iOS devices--> | |
| <meta name="viewport" content="initial-scale=1, maximum-scale=1,user-scalable=no"> | |
| <title>Page through records</title> |
RIDOLFI Inc. is a Seattle-based environmental engineering and consulting firm and is offering a paid GIS Internship for the summer of 2013. Responsibilities include a variety of tasks such as: data development, map creation, spatial analysis, and programmatic workflow automation. In addition to GIS support, current projects are focused in the following service lines:
| class tester(object): | |
| def __init__(self, val): | |
| self.val = val | |
| print 'created in %s' % self.val | |
| def __del__(self): | |
| print 'deleted' | |
| cache = {} | |
| def f1(): |
| >>> from shapely.geometry import mapping, shape | |
| >>> from shapely.geometry import Point | |
| # Create a class that has a __geo_interface__ | |
| >>> class MattPoint(object): | |
| ... __geo_interface__ = {'type': 'Point', 'coordinates': (0.0, 0.0)} | |
| ... | |
| >>> myPoint = MattPoint() | |
| >>> myPoint.__geo_interface__ | |
| {'type': 'Point', 'coordinates': (0.0, 0.0)} |
This d3 visualization represents Scott Rao's great graph depicting the relationship between different coffee brew methods and flavor profiles.
Brew methods to the left have the Most Body, Least Flavor Clarity, while brew methods to the right have the Least Body, Most Flavor Clarity. Larger circles represent a greater range in filter porosity.
Specifically, this is based on, via, and ripped off from.
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:
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 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.
