Learning how to complete your ArcGIS Geoprocessing steps using Python will allow you to reduce the time spent on complex and/or repetitive tasks and will enable your staff to learn a more productive and dynamic pathway to return results.
So the question is; which course is for you?
The Introduction to Geoprocessing Scripts Using Python (10.2)course will teach you how to create Python scripts to automate tasks related to data management, feature editing, geoprocessing and analysis, and map production using ArcGIS. You will also learn how to share your Python scripts so your key GIS workflows are accessible to others. This course is designed for GIS analysts, specialists, data processors, and others who want to automate ArcGIS tasks and workflows.
Although I can’t call myself a Python expert, from time to time I come across some interesting workflows worth sharing through this blog. This short story will be my first post on using Python to solve some non-standard tasks in ArcGIS for Desktop 10.2.1
This blog was actually created from a support incident where one of my clients asked me how to export the raster datasets that are stored within a file geodatabase, and referenced in a “Raster” attribute field, into a set of external files stored in one of the common files “. TIF” or “.JPEG.”
Ok, so here are a few pointers:
1. Imagine that we have a geodatabase with a point feature class and it contains two important attribute fields [image] and [name] (raster and text field types accordingly).
Visualizing change over time is an effective way to analyze local or global trends and predict future scenarios.
In GIS, much information of this type—for example, sea-surface temperature, annual births, labour market statistics, vegetation type, land-use data—is commonly stored in raster format, and it’s very useful to see these data animated. So how do you make your rasters time-aware? An easy way to is to take advantage of two new features available in Version 10: time-enabled layers and the mosaic dataset.
Follow these steps to create an animation showing change in sea surface temperature over time, or vegetation change in South-West QLD. There’s a tonne of data to show change over time—and now we have an easy way to visualize it in ArcGIS!
Your organization may have a collection of aerial photography from three years, such as 1995, 2005, and 2008 and you want to publish these as a web service. These may have different resolutions, such as 1 meter, 2 feet, and 0.5 feet. The earliest collection could be a panchromatic in a Geographic projection and the other two are color in a UTM projection.
The best way to manage this data is as separate source Mosaic Datasets and Derived Mosaic Datasets in a Geodatabase. Using source and derived Mosaic Datasets generally makes the management easier while maintaining best performance.