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.
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.
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.
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.
This analysis will use-high resolution satellite imagery to measure the effect that these forces have had on the amount of vegetative landcover and the amount of roofs with a reflective silver coating.
This analysis will use spatial statistical analysis techniques in R to investigate possible correlated data, and the extent to which observed clustering is statistically significant.
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.
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.
This post is the record of an in-class tutorial from April 2019, introducing scikit-learn for both geospatial and non-geospatial data. It is preserved as it was taught — the Anaconda setup, pinned package versions, and scikit-learn API calls below all reflect the ecosystem as it stood at the time, and the code will not run as-is in a modern Python environment. Treat it as a snapshot of the workshop rather than a maintained tutorial.
This analysis will use-high resolution satellite imagery to measure the effect that these forces have had on the amount of vegetative landcover and the amount of roofs with a reflective silver coating.
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.
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.
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.
This post is the record of an in-class tutorial from April 2019, introducing scikit-learn for both geospatial and non-geospatial data. It is preserved as it was taught — the Anaconda setup, pinned package versions, and scikit-learn API calls below all reflect the ecosystem as it stood at the time, and the code will not run as-is in a modern Python environment. Treat it as a snapshot of the workshop rather than a maintained tutorial.
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.
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.
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.
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.
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.
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.
Originally published on the Hit the Sticks dev blog in September 2012, archived here.
In preparing the backend network for our new turn-based strategy game, Just Tactics, we architected it to be able to easily scale up as the game becomes more popular. Step one was designing an automated netboot operating system environment, to allow us to bring up new servers without having to sit through an install procedure, and remove our dependence on mechanical storage devices.
Originally published on the Hit the Sticks dev blog in August 2012, archived here.
A major part of the network for our new turn-based strategy game, Just Tactics, is the pool of game servers. As the user base grows, this will have to scale up first. In designing our network, one important consideration was the ability to add new machines to the pool of game servers as easily as possible. To assist with this, they are all booting the same shared linux image over the internal network. With no configuration needed on the new server, it will look for a DHCP server, which will supply network configuration, and information on where to look for files necessary to boot. It will then download and boot a minimal operating system, and start the game server.
Originally published on the Hit the Sticks dev blog in September 2012, archived here.
In preparing the backend network for our new turn-based strategy game, Just Tactics, we architected it to be able to easily scale up as the game becomes more popular. Step one was designing an automated netboot operating system environment, to allow us to bring up new servers without having to sit through an install procedure, and remove our dependence on mechanical storage devices.
Originally published on the Hit the Sticks dev blog in August 2012, archived here.
A major part of the network for our new turn-based strategy game, Just Tactics, is the pool of game servers. As the user base grows, this will have to scale up first. In designing our network, one important consideration was the ability to add new machines to the pool of game servers as easily as possible. To assist with this, they are all booting the same shared linux image over the internal network. With no configuration needed on the new server, it will look for a DHCP server, which will supply network configuration, and information on where to look for files necessary to boot. It will then download and boot a minimal operating system, and start the game server.
Originally published on the Hit the Sticks dev blog in September 2012, archived here.
In preparing the backend network for our new turn-based strategy game, Just Tactics, we architected it to be able to easily scale up as the game becomes more popular. Step one was designing an automated netboot operating system environment, to allow us to bring up new servers without having to sit through an install procedure, and remove our dependence on mechanical storage devices.
Originally published on the Hit the Sticks dev blog in August 2012, archived here.
A major part of the network for our new turn-based strategy game, Just Tactics, is the pool of game servers. As the user base grows, this will have to scale up first. In designing our network, one important consideration was the ability to add new machines to the pool of game servers as easily as possible. To assist with this, they are all booting the same shared linux image over the internal network. With no configuration needed on the new server, it will look for a DHCP server, which will supply network configuration, and information on where to look for files necessary to boot. It will then download and boot a minimal operating system, and start the game server.
Originally published on the Hit the Sticks dev blog in September 2012, archived here.
In preparing the backend network for our new turn-based strategy game, Just Tactics, we architected it to be able to easily scale up as the game becomes more popular. Step one was designing an automated netboot operating system environment, to allow us to bring up new servers without having to sit through an install procedure, and remove our dependence on mechanical storage devices.
Originally published on the Hit the Sticks dev blog in August 2012, archived here.
A major part of the network for our new turn-based strategy game, Just Tactics, is the pool of game servers. As the user base grows, this will have to scale up first. In designing our network, one important consideration was the ability to add new machines to the pool of game servers as easily as possible. To assist with this, they are all booting the same shared linux image over the internal network. With no configuration needed on the new server, it will look for a DHCP server, which will supply network configuration, and information on where to look for files necessary to boot. It will then download and boot a minimal operating system, and start the game server.
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.
This analysis will use-high resolution satellite imagery to measure the effect that these forces have had on the amount of vegetative landcover and the amount of roofs with a reflective silver coating.
This analysis will use spatial statistical analysis techniques in R to investigate possible correlated data, and the extent to which observed clustering is statistically significant.
This post is the record of an in-class tutorial from April 2019, introducing scikit-learn for both geospatial and non-geospatial data. It is preserved as it was taught — the Anaconda setup, pinned package versions, and scikit-learn API calls below all reflect the ecosystem as it stood at the time, and the code will not run as-is in a modern Python environment. Treat it as a snapshot of the workshop rather than a maintained tutorial.
This analysis will use spatial statistical analysis techniques in R to investigate possible correlated data, and the extent to which observed clustering is statistically significant.
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.
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.
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.
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
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.
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.
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.
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.
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.
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.
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.
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.
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
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
This analysis will use-high resolution satellite imagery to measure the effect that these forces have had on the amount of vegetative landcover and the amount of roofs with a reflective silver coating.
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.
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.
This post is the record of an in-class tutorial from April 2019, introducing scikit-learn for both geospatial and non-geospatial data. It is preserved as it was taught — the Anaconda setup, pinned package versions, and scikit-learn API calls below all reflect the ecosystem as it stood at the time, and the code will not run as-is in a modern Python environment. Treat it as a snapshot of the workshop rather than a maintained tutorial.
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.
