Claude M. Schrader

Claude M. Schrader

Previously: 14 year career in IT. Currently: working with GIS, machine learning, and raster imagery

Cartographic Design

This is a collection of maps I’ve created, mostly for a Cartographic Design class at Temple University, as part of their Professional Science Masters in GIS. Software used in this class included Adobe Illustrator, Mapbox, OpenStreetMap, and QGIS.

Trenton, NJ

For this map, I started with a qgis database containing a lot of unstyled geospatial data on Trenton New jersey, and Morrisville Pennsylvania. This work involved simplifying and styling this data into a map emphasizing certain elements. I chose to focus on the effects that rail and highway infrastructure have on the waterfront and greenspace.
Figure 1

Taiwan

After downloading OpenStreetMap data from Geofabrik using a QGIS plug-in, I created three maps to support a New York Times article on the Biyun Chan Temple in Taiwan.
Figure 2 Figure 3 Figure 4

Philadelphia, PA

This assignment involved tracing segments of a historic map of Fairmount Park in Philadelphia, as practice using the Pen Tool and placing text on a path in Abobe Illustrator.
Figure 5

This assignment was the result of a timed in-class test. Given some data from OpenStreetMap, I created a postcard showing a walk from Septa’s Jefferson Station to the Wood Street Steps on the Delaware River Waterfront in Philadelphia. This was done entirely in QGIS, and the finished product was provided in the form of svg and pdf files, both of which must open in Adobe Illustrator.
Figure 6

These three icons were traced from Photographs in Adobe Illustrator, using the pen tool and a mouse.
Figure 7 Figure 8 Figure 9

This map was created in Mapbox Studio using data on areas of Philadelphia with high poverty and poor access to food. This data was obtained from OpenDataPhilly.

The Full Map can be found here

Figure 10

Towanda, PA

This is a mock-up of a large folding map made in Adobe Illustrator. I focused on Towanda PA, and the Endless Mountains Region. The main purpose of this exercise was practice laying out a large map, so the text and visual elements are placeholders.
Figure 11

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Climate Change Maps

I recently contributed two very large maps to FEMA: Fear Environmental Mayhem Ahead, an exhibition whose goal was to draw attention to the looming crisis that climate change presents. We decided to create maps of flooded Philadelphia neighborhoods to demonstrate that this is not just a worry for small islands, but a problem that will affect us directly.

Figure 1

Part 2: Creating a Classified map of the Colombian Wetlands using Keras and and Tensorflow

This is Part two of a report on a capstone project performed with Victor Gutierrez-Velez in the Remote Sensing and Sustainability Lab at Temple University. We will discuss our work expanding and customizing the model created by reachsumit in order to classify imagery of the Colombian wetlands obtained from Planet Labs.

Part One of this report provided an introductory tutorial on training and using a Deep Learning model to create a classified map using image segmentation.

Part 1: TUTORIAL: Creating a Classified Map Using Keras and Tensorflow

In my research with Victor Gutierrez-Velez in the Remote Sensing and Sustainability Lab at Temple University, this project involved investigating the applicability of Deep Learning and Neural Networks for automatically classifying high-resolution multi-spectral remote sensing imagery of wetlands in Colombia. This work was performed as part of my capstone project as a culmination of a Professional Science Master’s in GIS from the Department of Geography and Urban Studies at Temple University.

This post contains Part 1 of the report, and will walk through building a model using Python, Keras, and Tensorflow, and creating a classified map using code created by github user reachsumit.

Controlling ArcGIS Desktop with Python

Intro

As part of Temple University’s GIS Application Development with Python class, this project was completed as part of the final project. The goal was: create one python script to download and decompress all necessary data, use the ArcPy package to perform some spatial analysis with ArcGIS Desktop, and create a map using the arcpy.mapping module. The mapping module is pretty limited, so the programmatically created map is not a finished product.

I decided to analyze the distribution of healthy corner stores within 300 Meters of each school in Philadelphia using publicly available data.

Figure 1