Tag

GIS

Mar 08
0

Reading and discussion group: Spatial Analysis in Social Sciences

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This reading group moderated by consultants from CSCAR will focus on spatial analysis especially as practiced in social sciences. We will meet for 1.5 to 2 hours every month on the fourth Thursday and discuss one or two chapters from relevant graduate level textbooks. We will focus on the concepts and applications but will also try to discuss the technical details. The format is open-ended, and the key objective is to support learning at different knowledge and skill levels. If there is interest, we will also cover software implementation of techniques in R or Python. We will select reading material that is available via U-M library or freely accessible online.

The details for the third meeting are below.

Date – March 24, 2022

Time – 2:00 pm to 4:00 pm

Readings – We will discuss the following chapters:

(1) Chapter 4: Diagnosing Spatial Dependence (from Spatial Analysis for the Social Sciences by David Darmofal)

(3) Chapter 5: Diagnosing Spatial Dependence in the Presence of Covariates (from Spatial Analysis for the Social Sciences by David Darmofal)

Digital copies of the book are available from the UM Library.

Jan 31
0

Reading and discussion group: Spatial Analysis in Social Sciences

By |

This reading group moderated by consultants from CSCAR will focus on spatial analysis especially as practiced in social sciences. We will meet for 1.5 to 2 hours every month on the fourth Thursday and discuss one or two chapters from relevant graduate level textbooks. We will focus on the concepts and applications but will also try to discuss the technical details. The format is open-ended, and the key objective is to support learning at different knowledge and skill levels. If there is interest, we will also cover software implementation of techniques in R or Python. We will select reading material that is available via U-M library or freely accessible online.

Readings – We will discuss the following chapters:

(2) Chapter 3: Global and local indicators of spatial association (from Spatial Analysis using Big Data by Yoshiki Yamagata and Hajime Seya)

(3) Chapter 3: Spatial autocorrelation and statistical inference (from Spatial Analysis for Social Science by David Darmofal)

Digital versions of the above two books are available from the UM Library.

Nov 04
0

Introduction to Google Earth Engine – II

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Google Earth Engine (GEE) combines a multi-petabyte catalog of satellite imagery and geospatial datasets with planetary-scale analysis capabilities. The instant availability of data, massive compute power, and well-developed API make it a very convenient and powerful platform for geospatial analysis.

GEE provides native APIs in JavaScript and Python. However, recently the user community has developed a package “rgee (https://github.com/r-spatial/rgee)” that allows R users to interact with GEE (via reticulate and Python) and utilize its functionalities.

This workshop will focus on using R (the “rgee” package) to interface with GEE and utilize its power for ultra-fast geospatial analysis. You should attend the first workshop on November 18, if you are new to GEE.

Some familiarity with remote sensing and GIS, and exposure to raster and vector data analysis will be helpful.  You will need to register (free) at signup.earthengine.google.com with Google to use the Earth Engine. Please use your UM email account to register.

Nov 04
0

Introduction to Google Earth Engine – I

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Google Earth Engine (GEE) combines a multi-petabyte catalog of satellite imagery and geospatial datasets with planetary-scale analysis capabilities. The instant availability of data, massive compute power, and well-developed API make it a very convenient and powerful platform for geospatial analysis.

GEE provides native APIs in JavaScript and Python. However, recently the user community has developed a package “rgee (https://github.com/r-spatial/rgee)” that allows R users to interact with GEE (via reticulate and Python) and utilize its functionalities.

The two hands-on workshops will introduce GEE and show how to leverage its capacity for spatiotemporal analysis and visualization in R. The first workshop (November 18) is an introduction to GEE and we will primarily use JavaScript API to learn the basics of GEE. The second workshop (November 22) will focus on using R (the “rgee” package) to interface with GEE and utilize its power for ultra-fast geospatial analysis.

Some familiarity with remote sensing and GIS, and exposure to raster and vector data analysis will be helpful.  You will need to register (free) at signup.earthengine.google.com with Google to use the Earth Engine. Please use your UM email account to register.

Sep 01
0

GIS and Spatial Analysis Fundamentals – IV (Map visualization)

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This is the fourth workshop about the fundamentals of GIS and spatial analysis this semester. Each workshop covers one or two key elements and is self-contained. The focus is on conceptual details that can provide sufficient preparation for applications, but we will also touch upon the technical aspects. Most workshops will have at least one hands-on exercise. Typically, each workshop is divided into one hour of lecture-style presentation and half an hour of hands-on exercises. Unless mentioned otherwise, we will use R and/or QGIS.

 

This workshop will focus on basic cartography principles for map-making and explore the functionalities of R and QGIS for making production-quality single- and bi-variate static and dynamic choropleth map. We will also explore the functionalities of leaflet, a powerful JavaScript library, to create web maps and add extra information via elements such as pop-ups.

 

Participants should have some familiarity with R, but exposure to QGIS is not required.

Sep 01
0

GIS and Spatial Analysis Fundamentals – III (Geocoding)

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This is the third workshop about the fundamentals of GIS and spatial analysis this semester. Each workshop covers one or two key elements and is self-contained. The focus is on conceptual details that can provide sufficient preparation for applications, but we will also touch upon the technical aspects. Most workshops will have at least one hands-on exercise. Typically, each workshop is divided into one hour of lecture-style presentation and half an hour of hands-on exercises. 

 

Geocoding (or sometimes reverse geocoding) is often a very first step in many geospatial analyses. There are many options available for geocoding with different degree of accuracy. A basic understanding of the process helps you in choosing the best option. The workshop will cover basic concepts in geocoding, different open-source and proprietary options available, accuracy and reliability in geocoding, and best practices. We will use R and ArcGIS.

Sep 01
0

GIS and Spatial Analysis Fundamentals – II (Data models: vector, network)

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This is the second workshop about the fundamentals of GIS and spatial analysis this semester. Each workshop covers one or two key elements of GIS and spatial analysis and is self-contained. The focus is on conceptual details that can provide sufficient preparation for applications, but we will also touch upon the technical aspects. Most workshops will have at least one hands-on exercise. The first one hour of the workshop is a lecture-style presentation, followed by the next half-hour for the hands-on exercises. Unless mentioned otherwise, we will use R and/or QGIS for the hands-on portion. 

 

How data is recorded in digital systems has significant implications for accuracy, algorithms, and the type of analyses that can be undertaken.  In this workshop we will cover data structure for vector and network data in the context of a 2-D GIS system. The focus is on developing a basic understanding of elements such as essential primitives, how more complex objects are derived from the primitives, and different formats and file systems. 

 

Participants should have some familiarity with R, but exposure to QGIS is not required.

Sep 01
0

GIS and Spatial Analysis Fundamentals – I (Coordinate system)

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This is the first workshop about the fundamentals of GIS and spatial analysis this semester. Each workshop covers one or two key elements of GIS and spatial analysis and is self-contained. The focus is on conceptual details that can provide sufficient understanding for applications, but we will also touch upon the technical aspects. Most workshops will have at least one hands-on exercise. Typically, each workshop is divided into one hour of lecture-style presentation and half an hour of hands-on exercises. Unless mentioned otherwise, we will use R and/or QGIS for the hands-on portion. 

 

There are 100s of coordinate systems and datums available in modern software that provide GIS functionalities. A basic understanding of different coordinate systems, their strength and limitations, and conversion between different systems are essential for choosing the right system and manipulating geographically referenced data. In this workshop we will cover basics of coordinate systems for 2-D GIS from an applied perspective. 

 

Participants should have some familiarity with R, but exposure to QGIS is not required.

Jul 13
0

Geostatistics – III

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Many environmental variables such as temperature, rainfall, air pollutants, and soil nutrients are measured at sampled point locations. We often need to estimate these variables at one of more unsampled locations. Geostatistics provide tools and techniques to carry out this task.

In a series of three workshops, we will cover the basics of Geostatistics. In this third workshop, we will combine the material we covered in the first two workshops and develop the geostatistical modeling approach. This is mainly a lecture style workshop, but will include an example in R. The material will also help you understand the basics of Gaussian Process Regression, a commonly used modeling technique in Machine Learning.

Jun 15
0

Geostatistics – II

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Many environmental variables such as temperature, rainfall, air pollutants, and soil nutrients are measured at sampled point locations. We often need to estimate these variables at one of more unsampled locations. Geostatistics provide tools and techniques to carry out this task.

In a series of three workshops, we are covering the basics of Geostatistics. In this second workshop, we will focus on covariance and variogram, and their estimation in the context of geostatistical modeling. This is mainly a lecture style workshop, but we will also execute some examples in R. The material will also help you understand the basics of Gaussian Process Regression, a commonly used modeling technique in Machine Learning.