Posts by Tag

philadelphia

Controlling ArcGIS Desktop with Python

2 minute read

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

Arcadia Commons

less than 1 minute read

I’m on the board of Arcadia Commons, a local nonprofit formed in 2013 to protect and care for greenspaces around East Kensington. This is a large poster I created to help visualize our area of focus, and the organization’s mission.

Figure 1

ETL Transformation to Third Normal Form for Philadelphia Building Demolition Permits

19 minute read

Figure 5

This ETL transformation was performed as an exercise in demonstrating proficiency using SQL in the PostgreSQL relational database with the PostGIS spatial extensions. I selected a spatial dataset of building demolition permits to load into the database and normalize to Third Normal Form. Once in place, this data can be combined with a secondary dataset on children with elevated blood lead levels to investigate correlations between demolition activity and elevated lead levels.

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machinelearning

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

14 minute read

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

6 minute read

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.

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python

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

14 minute read

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

6 minute read

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

2 minute read

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

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arcgis

Controlling ArcGIS Desktop with Python

2 minute read

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

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R

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design

Arcadia Commons

less than 1 minute read

I’m on the board of Arcadia Commons, a local nonprofit formed in 2013 to protect and care for greenspaces around East Kensington. This is a large poster I created to help visualize our area of focus, and the organization’s mission.

Figure 1

Cartographic Design

1 minute read

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.

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qgis

Arcadia Commons

less than 1 minute read

I’m on the board of Arcadia Commons, a local nonprofit formed in 2013 to protect and care for greenspaces around East Kensington. This is a large poster I created to help visualize our area of focus, and the organization’s mission.

Figure 1

Cartographic Design

1 minute read

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.

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illustrator

Arcadia Commons

less than 1 minute read

I’m on the board of Arcadia Commons, a local nonprofit formed in 2013 to protect and care for greenspaces around East Kensington. This is a large poster I created to help visualize our area of focus, and the organization’s mission.

Figure 1

Cartographic Design

1 minute read

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.

Back to top ↑

database

ETL Transformation to Third Normal Form for Philadelphia Building Demolition Permits

19 minute read

Figure 5

This ETL transformation was performed as an exercise in demonstrating proficiency using SQL in the PostgreSQL relational database with the PostGIS spatial extensions. I selected a spatial dataset of building demolition permits to load into the database and normalize to Third Normal Form. Once in place, this data can be combined with a secondary dataset on children with elevated blood lead levels to investigate correlations between demolition activity and elevated lead levels.

Introduction to Using MongoDB with Spatial Data

9 minute read

NoSQL databases are unlike many traditional SQL databases. In this tutorial we will review the differences between NoSQL and relational databases such as PostgreSQL, the strengths and weaknesses of a NoSQL database, and the reasons you might choose to use or avoid a NoSQL database. We will also demonstrate executing both spatial and non-spatial queries in MongoDB

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keras

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

14 minute read

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

6 minute read

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.

Back to top ↑

tensorflow

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

14 minute read

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

6 minute read

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.

Back to top ↑

deeplearning

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

14 minute read

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

6 minute read

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.

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test

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statistics

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privacy

Issues Surrounding the Release of Cellular Location Data

14 minute read

Through Spring and Summer 2018, a network of companies buying and selling access to the location of every customer of the four major carriers has been partially unveiled. Despite clear federal regulations governing receiving consent before providing access to customer data, some companies had rather loose consent policies.

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writing

Issues Surrounding the Release of Cellular Location Data

14 minute read

Through Spring and Summer 2018, a network of companies buying and selling access to the location of every customer of the four major carriers has been partially unveiled. Despite clear federal regulations governing receiving consent before providing access to customer data, some companies had rather loose consent policies.

Back to top ↑

nosql

Introduction to Using MongoDB with Spatial Data

9 minute read

NoSQL databases are unlike many traditional SQL databases. In this tutorial we will review the differences between NoSQL and relational databases such as PostgreSQL, the strengths and weaknesses of a NoSQL database, and the reasons you might choose to use or avoid a NoSQL database. We will also demonstrate executing both spatial and non-spatial queries in MongoDB

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spatial

Introduction to Using MongoDB with Spatial Data

9 minute read

NoSQL databases are unlike many traditional SQL databases. In this tutorial we will review the differences between NoSQL and relational databases such as PostgreSQL, the strengths and weaknesses of a NoSQL database, and the reasons you might choose to use or avoid a NoSQL database. We will also demonstrate executing both spatial and non-spatial queries in MongoDB

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remotesensing

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sql

ETL Transformation to Third Normal Form for Philadelphia Building Demolition Permits

19 minute read

Figure 5

This ETL transformation was performed as an exercise in demonstrating proficiency using SQL in the PostgreSQL relational database with the PostGIS spatial extensions. I selected a spatial dataset of building demolition permits to load into the database and normalize to Third Normal Form. Once in place, this data can be combined with a secondary dataset on children with elevated blood lead levels to investigate correlations between demolition activity and elevated lead levels.

Back to top ↑

etl

ETL Transformation to Third Normal Form for Philadelphia Building Demolition Permits

19 minute read

Figure 5

This ETL transformation was performed as an exercise in demonstrating proficiency using SQL in the PostgreSQL relational database with the PostGIS spatial extensions. I selected a spatial dataset of building demolition permits to load into the database and normalize to Third Normal Form. Once in place, this data can be combined with a secondary dataset on children with elevated blood lead levels to investigate correlations between demolition activity and elevated lead levels.

Back to top ↑