How To Basemap in Q
HOW TO How To Add a Basemap In QGIS Will all the transitions in our lives happening right now I figured it might be best to share a how to on something I’m definitely going to find useful in the coming months, QGIS. Something That I have taken for granted in ArcPro and ArcMap is the presence of pre-loaded basemaps. Usually they are convenient for quickly visualizing where your data is in the world, but oftentimes they don’t fit the theme of your data or get in the way visually. I usually found myself scrapping basemaps at some point in the GIS process in order to visualize my data as the list of available maps never quite suited what I needed in particular projects. In QGIS there is no built-in functionality for basemaps, but luckily as I’m quickly finding out there is a plug in for everything. (NOTE: This is just one way to do this, as in anything in GIS there are many ways to accomplish the same task) You will need to go into the plugin tab at the top of the toolbar and select manage and install plugins. (By default you are only allowed to access plug ins that have been thoroughly vetted by the community but you can always change this to be able to add any plug in from a repository). From this window search a plug in called “Quick Map Services” and install it. When it installed a tool called QMS will appear on your toolbar (Top Right). This tool functions almost exactly like the “Add Basemap” tool in ArcPro only you can choose to add pretty much any basemap you could ever think of (Including ESRI’s own maps).
Literature Review 3
Review: The direct impact of landslides on household income in tropical regions: A case study from the Rwenzori Mountains in Uganda https://search-proquest-com.proxy.wm.edu/docview/1769623954/2F54E874A8144397PQ/1?accountid=15053 Key Concepts - Cross sectional household survey and GIS data was used to obtain information on the economic status of household sin the Rwenzori Mountains - Income of affected households is significantly reduced in the wake of landslide events for several years - Self-employment or wage employment significantly increases for these individuals, but these activities are usually not enough to compensate for the loss of agricultural revenue - These landslides are localized and thus in this region there are not risk sharing measures in place to lessen the impact How GIS Was Used - GPS data at the household level was collected along with the questionnaire to tie economic status and activities with a spatial location - Landslide susceptibility was proxied with Geographical data including slope steepness, lithology (rock type) and, water flow - A DEM and lithology data were used in conjunction with ArcGIS’s Flow Accumulation tool to calculate a relative flow value - This GIS data was plugged into an equation as “susceptibility” to measure an approximation of household welfare - Map layouts were created to overview the study areas Here is another paper concerning economic impacts of landslides. Specifically, it estimates the impact on household income and mitigation strategies for this region. The study found that 85% of these relatively poor household’s income is derived from agriculture and about 40% of them have been directly impacted by landslides in the past 15 years. Interestingly there was no significant difference in income levels of affected and unaffected households (over 15 years). This was not the case for more recent landslides. In the year following a landslide significant economic impact was felt and homeowners turned to other sources of income to supplement agricultural income loss. Lower income individuals with smaller plots of land are more at risk for landslides. This was found by analyzing these individuals against those with larger plots of land. The authors found that larger land plots increased resistance to negative economic impact. In addition, it seems that there were no formal measures put in place to help those affected by these landslides at any point in time over the past 15 years. The authors conclude by recommending two mitigation strategies. First, they advise for more attractive stable jobs outside agriculture should be pressed into the region. As of now those affected by landslides only temporarily transition to these kinds of jobs indicating that they are less preferable to agriculture. Second, they recommend the development of local disaster relief funds as no such savings funds exist at this level. These funds can be used to lessen the economic impact on the members of the community most affected by landslides.
Literature Review 2
Literature Review 2 Coming at You
Review: The Economic Impact of Landslides and Floods on the Road Network https://www.sciencedirect.com/science/article/pii/S1877705816306154 Key Concepts - Landslides have significant impacts on transportation to and from remote communities - Road networks can be severely impacted or completely destroyed as a result of landslides - This destruction results in direct and indirect economic impacts on remote communities - Analysis of the impact of landslides can be undertaken by looking at road networks rather than the isolated areas of the landslide itself How GIS Was Used - The transportation network was mapped in Scotland and used to analyze a “vulnerability shadow” - This shadow was made into a map layout displaying the relative size of a small landslide event and very large economic vulnerability shadow - Diversionary routes due to the landslide were mapped in a program called QUADRO This article focused on the impacts of landslides on road networks and access to economic activities. In 2004 Scotland experienced significant debris flow event due to excessive rainfall. This event caused road networks to be strained and some remote communities to be cutoff entirely. The authors specify that there are several categories of impact associated with events like this. By breaking up costs in this way the impact of the landslide event can be quantified. Direct economic impacts: direct costs associated with repair and cleanup Direct consequential economic impacts: “Disruption of Infrastructure”, closures of roads, cost of injuries, cost of detours Indirect consequential economic impacts: These are more abstract costs like the viability of business in areas cutoff by road closures, strain on industries and production associated with landslide events. Ultimately this article was a brief introduction into how landslides can be thought of as affecting a larger area than the localized region of the landslide itself. By using the concept of a “vulnerability shadow” tied to road network this area can be easily viewed and analyzed. This framework can be used in other susceptible areas to predict costs to infrastructure and less tangible costs associated with road network closures and detours.
Literature Review 1
Here is the first in a series of literature reviews
Review: Direct impacts of landslides on socio-economic systems: a case study from Aranayake, Sri Lanka https://link.springer.com/article/10.1186/s40677-018-0104-6 Key Concepts - Landslides have huge socioeconomic impacts that are oftentimes difficult to quantify. - This study uses a case from Sri Lanka to provide a framework for studying these impacts - Questionnaire from residents to determine economic impacts at a household level - Satellite Imagery was used to find affected land and homes - A regression model was used to estimate the economic value of the affected gardens How GIS Was Used - Satellite imagery was used to determine land use and affected homes - Satellite imagery was also used to demarcate the extent of the landslide boundaries - It was also used to make map layouts denoting land use - Field studies using GPS were also used to map affected households and the landslide boundary as a ground truth for Google Earth images - The socioeconomic data gathered from questionnaires was incorporated into this GIS data This work attempted to quantify the socioeconomic impacts of a particular landslide in the Aranayke region of Sri Lanka. A landslide occurred there in May of 2016 resulting in the deaths of 127 individuals and significant damage to two villages. The region is heavily dependent on Kandyan home gardens (or KHGs). They make up 52% of economic household income and many of the gardens in the area were damaged or completely destroyed. Because these gardens are located almost entirely on slopes in the country, they represent a significant source of further socioeconomic risk. Ultimately the study concluded that the region was producing up to $160,000 in GDP through the use of these gardens before the landslide. All of the gardens were affected by this landslide in major or minor ways. In addition, the study found that almost all of the gardens in this region were threatened by future landslides. Approximately $40,000 of damage was done to the region in this one event. This study serves as a fantastic case for further analysis of the economic impact of landslides in this region and other regions. By using a combination of field surveys and remote sensing data/satellite imagery the authors produced a compelling quantifiable estimate of damages. It’s not unreasonable to apply this approach to other areas at risk for or already affected by landslide disasters. Studies like this can help convince local officials that landslide preparation is in the best interest of the community both morally and economically.
Scrap That, Start Again (x4)
SUPER Gear Switch, Corona Virus Edition
Spring Break is upon us and I took the opportunity to travel a bit before hunkering down today in Swem to get some work done. Little did I know when I made these plans that Spring Break would be extended by quite a bit. Regardless, I did end up making it in to the library this morning for a few hours. Because I had to miss the toolbox lecture due to work I decided today to attempt to work on that aspect of the project. As such, I read the blog entry and downloaded the toolbox to begin looking it over. If I'm being honest I don't understand quite a bit of it and I'm very frustrated that the toolbox session for this Sunday had to be cancelled. That being said I did end up running your toolbox with the data I had. Looking forward I think most of tomorrow and this weekend will be spent making sure I've hit these learning goals. Initally I was going to spend time working on a presentation for Monday's session but it seems like that isn't happening anymore so I'm going to focus more on my own learing rather than a consultant presentation.
Scrap That, Start Again (x3)
Follow Along With California
Today I went over the Colorado Case study and tried to follow along using my own data from California. This went swimmingly until I realised that my MUSYM keys were completely different from the Colorado data set. I spent a very long time trying to fix this problem and looking up what these codes (instead of numbers) could mean, but ultimately I decided that switching gears once again and getting some sort of progress was more important than using my own data. So, from now forward I decided to use the Colorado data to simplify my life. I followed along the brute force method to create C, Degrees, and Yr values for the Colorado table. Initially I wanted to use my very own code in an if/else statement to select the needed fields, but the interface for arc pro really confused me. I spent some time looking up basic coding principles/watching tutorials on how these statements worked and I really thought that I had a good idea of what to do, but every time I ran the code in Arc Pro it kicked back errors. It seemed simple enough to me but I guess I was wrong. I'm glad I tried it however as I did learn some about the coding process. In other news I began reading the literature on other models unrelated to the Critical Rainfall Threshold. I doubt I will be able to incorporate or run this model, but getting an idea for a model different than the one we are currently using might prove useful (and it happens to be a learning requirement).
Scrap That, Start Again (x2)
A Lot Got Done Today, Though it Might Not Seem Like it
Today we switched gears. A new project road map was given to us and we began attempting a basemap for the capital of Jordan, Ammam. I spent a few hours of looking up utilities data, road data, and building data to create the basemap and researching a recent landslide that destroyed a highway connecting the capital to another large city in the country. All of this went well, but due to the difficulty of finding data and the time constraints on this project, I've decided to switch gears and persue the same project in the United States first. This test run of the model will focus on southern California along the senic State Route 1. I chose this area for a few reasons. Most importantly the amount of data available for southern California is leaps and bounds more than Jordan, Bahrain, or French Polynesia. Second, the highway is a historic and beloved route connecting up almost the entire coast of California. Finally, this route has seen its fair shair of landslide events recently and in the past. The road itself is often closed and requires multi million dollar renovations and hours long detours for the small towns along its path every time a landslide occurs. Because of this, it is a perfect testing ground for the economic impact of landslides and our model as a whole.
In other news I began formulating an ethical discussion focusing on where we as consultants get our data. The book "Weaponizing Maps Indigenous Peoples and Counterinsurgency in the Americas" discusses the ethical implications of mapping indigenous groups using colonial and counterinsurgency techniques. The particular story I read and created a discussion about involves a few indigenous communities in the Sierra Juárez range in Mexico. These communities were exploited by the Foreign Military Studies Office of the United States Army in the early 2000's, in order to test counterinsurgency techniques. This story brings up questions concerning where we get our data, and who funds the process of collecting data. Next week I will be discussing this.
The Model
How Are We Conducting Our Analysis?
The Model
tan φ C - ψt * γw * (tan φ) Fs = ----- + -------------------------- tan θ γr * H * (sin θ) * (cos θ)
Where:
Fs is the factor of safety
φ is internal angle of friction (deg)
θ is hillslope (deg)
C is soil cohesion (Pa = kg/m/s^2)
ψt is pressure head (m); h/cosθ
γw is unit weight of water (N/m^3); N=kg*m/s^2
γr is unit weight of soil regolith (N/m^3)
H is soil regolith thickness (m)
Today we went over this formula and began gathering data to impliment our geospatial solutions. We're excited to begin working for you!
Welcome to Slope Consulting
We are professional consultants dedicated to mitigating the negative economic effects of landslides.
Welcome! Here is the home of Slope Consulting, Our website was heavily edited today Monday February 10th. Don't worry though! We are still as dedicated as ever for all your consulting needs.
Slope Consulting Site Launch
Welcome to Slope Consulting's Site Launch!! (This post has been transfered from our original website)
Here is an image