Julieta's GIS Blog

This week we were introduced to MEDS, which stands for Minimum Essential Data Sets. It is a series of minimal essential data sets are critical to the success of a HLS operation.   It consists of the following 8 minimal data sets: Ortho imagery Elevation Hydrography Transportation Boundaries Structures Land cover Geographic Names Due to the data used being critical to safety operations, there are minimal goals for resolution, accuracy, and currency in place for both Urban and Large areas.  For our lab this week, I prepared a MEDS GIS that helps identify key data sets to prepare for a Boston Marathon bombing crisis scenario. Started by organizing a new map based on a Boston Geodatabase. Then created 7 different group layers to include ortho imagery, DEMs, geographic names, transportation, hydrology, boundaries, structures, and land cover data. Given a specific study area helped identify only the Boston Counties affected along with geographic sites...

This week we start look at how GIS is used for solving homeland security and law enforcement decisions. This first assignment focuses on how crime is analyzed and managed using GIS by performing a crime analysis for Washington D.C. using data from the DC Metropolitan Police Department . For the first map deliverable, I Geocoded Police Stations from table data in order to create a point shape file for a Police Stations layer. Using a Distance Buffer and Spatial Join for Crime proximity, I was able to determine that higher populated areas do not necessarily mean higher crime rate. However, for some reason, there’s more crimes committed closer to Police stations. A majority of crime tends to occur within 1 mile of Police Stations according to the pattern displayed. Police stations are displayed by percentage of crime activity. The Police Stations with the highest crime percentages are Sixth District (6D), Seventh District (7D), and Third District (3D). 6D observes a lot of theft type ...

This week's assignment closes our section in natural hazards with Hurricane Sandy. Using data from NOAA, FEMA, and state of New Jersey I was able to first map Hurricane Sandy's path then take a closer look at the damage caused in Ocean County, NJ. The map below was created using table data detailing storm information to create a storm path, which was them symbolized to display how it changed from start to end. I also performed a Storm Damage Assessment using NOAA, FEMA, and state of New Jersey data. The most important step for this assessment was putting together two raster mosaics, one pre-storm and one post-storm, of aerial imagery for the study area. I was able to create a chart of damage done to one side of Fort Ave using the two mosaics and ArcGIS's Effects tool.

This week's lab had us create an evacuation map for the tsunami in Fukushima, Japan. Part of it also served as refresher in best practices for setting up a new Geodatabase using only base data files. Which meant I had to organize different feature classes into feature datasets to improve productivity. There is a lot of data involved for a natural disaster scenario and it drove home the importance of maintaining specific feature datasets within the geodatabase. One of my favorite steps in this lab involved the Fukushima and Sendai area DEMs that helped generate Evacuation Zones 1-3, not pictured in the final map in order to create a final affected coastal zone DEM. I had never created a model as large as the one for this step in model builder, it certainly saved a lot of time. What I've taken away from this scenario is that whichever method of risk assessment is used, the GIS specialist has to be prepared with the data organized and ready to present during an emergency in o...

For our first lab of this course we had to identify potential hazard zones for the Mt. Hood area in Oregon.  I was able to create a final stream feature using tools in the  Spatial Analyst Extension in ArcGIS, a 2011 USGS 30M DEM of the region, census block group data, and hydrology data. To do this, I had to convert a number of rasters using the original hydrology data and DEM to determine a flow. Then, using the census block data, I was able to determine which block groups were in hazardous areas based on whether they intersected the hazard stream at .5 a mile buffer. The final result is a population analysis for areas within hazard zones. As one can see, there is a strong concentration of people, cities, and schools Southwest of Mt. Hood.

After completing this GIS Internship course, I'll have one more course in order to complete my certification this summer. It's a great time to setup a portfolio to best showcase what I've learned and accomplished during this program and hopefully receive feedback from peers. It was a very daunting assignment when starting out. It required going back through my student blog to review which assignments I felt strongly about the most. I wound up testing different formats, menu order, and ways to implement numerous map assignments. Alas, my current final GIS portfolio can be found at  http://julieta-gis.net/ I decided to go with the digital option due to the amount of content and also because I wanted a chance to refresh my website making and modifying skills. A great benefit to using a site is that you can continue to add any sort of content in your portfolio section. This turned out to be a great assignment. Whether a peer made a paper or digital portfolio, this is some...

For this last module, we explored methods in Dasymetric Mapping. Dasymetric Mapping is "the process of disaggregating spatial data into finer units of analysis using ancillary data to help refine locations of population of another phenomena." This type of analysis basically helps look at where populations of a certain demographic(s) are concentrated within a set of boundaries. It may sounds like a fairly straight forward process, but it turned out more complex than expected... The first two parts of the lab were great exercises and stepping stones, per se, in introducing us to the process and mind set of actually perform a dasymetric analysis. First I performed an Areal Weighting analysis that required to produce a population estimate for areas within a Basin. This was followed by another Areal Weighting exercise this time with ancillary data which produced a population estimate for children within certain school districts. I will admit that the last part got the be...

This week's lab focuses on becoming familiar with the Modifiable Area Unit Problem (MAUP) and its two effects: scale and zonation. Mainly, I learned more about how people try to manage Gerrymandering. Gerrymandering is one of the best examples of the Zonation effect in the MAUP. This involves the splitting of Counties, usually unevenly, as means to exclude certain demographics to gain favor for a political party. A possible way to measure the effect it has on political districts would be to calculate how many Counties a District breaks up. Ideally, we want to lessen the amount of Counties broken up in this process. Depending on the County population, a standard measure can be established in order to even out Districts.

This was the first week addressing the topic of scale. The lab objective was to observe the effects of scale and resolution in spatial data properties for vector and raster data. First part of the lab required me to analyze differences in scale for hydrology features. Second, I had to compare two digital elevation models (DEMs), Lidar and SRTM, and figure out a method for comparing elevation data. I performed Slope, Aspect and Curvature analysis on the Lidar DEM at a resolution of 90m. Then performed Slope, Aspect, and Curvature analysis on the reprojected SRTM DEM also at 90m. This data allowed me to produce a chart listing averages for both DEMs in order to observe any trends. Notable inconsistencies were found in Average Slope degrees and Max elevation between the two DEMs. SRTM DEM had a lower measure at 29.4 average slope degrees, while the Lidar DEM was 31.3. Maximum elevation was higher for the Lidar DEM at 1063.05 than the SRTM DEM at 1053. DEM DEM resolution (mete...

This week we were introduced to Geographically Weighted Regression (GWR). The main differences between a GWR and an Ordinary Least Squares (OLS) regression are: Where OLS is a global model, GWR is local Distances are NOT considered in OLS, whereas distances define the data fitting GWR Coefficients are constant in OLS, while in GWR they vary by location I was a lot more comfortable making these regressions and comparing for best fit variable models this time around. The lab consisted first of an example in comparing an OLS and GWR analysis and learning about the significance of differences in their prediction values. The second part another OLS and GWR analysis, this time I worked somewhat 'off-rails'. The exercise started by selecting a crime type, I chose 'Residential burglary', then making it a feature class in order to continue an analysis to find out what types of variables had the strongest correlation to high 'Residential burglary' rates in...

The goal of this week's lab is to perform a series of multivariate regression analysis, examine and work with sophisticated diagnostics, and perform a regression analysis in ArcGIS. Last week's lab exercises served as a great refresher in basic statistics and introduction to simple regression analysis, which came in extremely useful in working through this lab. It involved a higher level of statistics comprehension. I started by carrying out a multivariate regression in Excel. Then compared multivariate models created in Excel. The last part of this lab required to use ArcGIS to perform an Ordinary Least Squares (OLS) analysis and regression diagnostics. I ran the OLS and Spatial Autocorrelation (Global Moran's I) tools three times to arrive at my final results for the best fit model. According to value result guidelines, I need to use the best fit model which consist of the highest Adjusted R-square and lowest AICc values. The first model that passed in Summary 4/8 con...

This week we had to produce a classified image from satellite data using the supervised image classification method. To do this, first, I identified fourteen land cover types based on an original satellite image for Germantown, Maryland using the Inquire Cursor and Signature Editor in ERDAS Imagine. Then modified the band colors to better suit the features being represented. From there, I re-coded each class down to have eight as there were a few land cover types that repeated. This process required to create two additional images that incorporated these classes: a supervised image, and a distance image. I did another color pass in the new supervised since it is easier to have unique colors for eight classes. The final part of this map was completed in ArcMap, both of my own new images are represented here. While the supervised image displays the required eight land cover types, the distance image serves to show feature variances in color. The features with the brightest pixels he...

This lab served as a great review of basic Statistics methods, then focusing on interpreting Regressions and Correlations. We started by reviewing basic Statistical functions in Excel, such as how to calculate Average, Median, Standard Deviation, etc. Then were introduced to the Data Analysis tool in Excel, which is super helpful in performing Descriptive Statistics, Correlations, and Regressions on different data sets from the lab. I’ve only ever taken an Introduction to Statistics course in College, so much of what we covered I recall having to do by hand on graph paper and calculator, fun times!... I see now that we will likely be performing some estimates in the case of missing data points. So I’m glad we got to cover this scenario in the last exercise in which we had to fill in estimated data using the Slope formula covered in lecture. I used the Slope formula Y = b*x + a in which b is the is the Slope .84, and a is the intercept coefficient 161.83, and x was my B column val...

This lab wraps up our modules to do with Surface analyses. Our first objective was to calculate and interpret the vertical accuracy of a DEM using NDEP accuracy guidelines. The second objective was to observe the effects of interpolation methods on a DEM's accuracy. In the first accuracy analysis sample points appeared to be laid out in clusters instead of a natural distribution in which certain Land Cover types exhibit common areas. I’m leaning towards estimated interpolation causing this type of distribution because there is a notable gap of data points in higher elevation areas. I was able to plot a total of 2387 points from a provided table using the Make XY Event Layer tool. Then clipped the data points to the DEM extent. In order to be able to perform an analyses of the DEM and the reference point dataset, I extracted elevation values from the DEM and created a new set of points using the Extract values to Points tool. Then used the Extract Multi Values to Points tool...

In our lab this week we delved more into Multispectral Analysis involving Thermal Imagery in order to learn how to interpret radiant energy. Using both ArcMap and ERDAS Imagine, I was able to create two different composite multispectral satellite images from TIFF files. For the final map assignment, I used the composite image created in Exercise 2 of this lab which I compiled in ERDAS Imagine. I had already identified a big fire site and then happened to find another one further down the image. As means to highlight the fire I modified the RGB bands to R-2, G-3, B-5 in the Landsat 4 TM satellite image. This made surrounding vegetation a dull blue while making the fire and its smoke orange.

Interpolation is utilized in order to give a raster cell a predicted number value. We use interpolation methods to create a value for unknown values basically. This is necessary for a number of different data gathering issues such as difficult environments or complex point data that could otherwise prove difficult to measure in person. We had the opportunity to examine and perform many types of spatial interpolations in this lab such as: Thiessen Inverse Distance Weighted Spline Working with a Water Quality point data set for Tampa Bay, we were required to perform surface interpolations to examine the distribution of Biochemical Oxygen Demand (BOD) in milligrams per liter. Below is the result for Thiessen. The darker polygons represent a lower BOD concentration, while white polygons represent a high concentration. This example represents Inverse Distance Weighted interpolation. The ranges here vary from light pink, high BOD concentration, to dark green, lower B...

We continued our lessons in ERDAS Imagine this week through lab exercises which taught us how to interpret histogram data of satellite images and how to identify features using said data. By the end, I had to have identified three different features that fit the following descriptions: In Layer_4 there is a spike between pixel values of 12 and 18.  Identify the feature that represents both A) a small spike in layers 1-4 around pixel value 200, and B) a large spike between pixel values 9 and 11 in Layer_5 and Layer_6.  In certain areas of water, layers 1-3 to become much brighter than normal, layer 4 becomes somewhat brighter, and layers 5-6 to remain unchanged. I used the Inquire cursor for the first feature as it points to pixel values. Having analyzed the histogram, it helped determine that the feature I'm looking for would be dark in color. The only consistently dark feature was deep water. There were two areas in the image with the brightest pixels but...

This week we explored Triangular Irregular Networks (TIN) and Digital Elevation Models (DEM) in greater detail to learn their differences, the type of applications they're typically used in, and what makes each unique when applied in a GIS analysis. I found the lab exercises to be extremely helpful in illustrating differences. For example, when you add points to represent data points to a DEM, there will be more points as opposed to a TIN which creates a point where necessary in order to highlight the most relevant data. Above is an example from an exercise which I think serves to gather a better idea of how data in TINs is distributed. The aqua contours represent contours created from a Spline done on a set of Points. Black and red contours represent TIN contours. A big difference I noticed right away was how spaced the contours created from points were in comparison to the contours from the TIN. The biggest differences appeared to be at lower elevations where the point c...

In this week's lab, we had the opportunity to enhance a satellite image using ERDAS Imagine and ArcMap. The initial image we worked with in ERDAS had a lot of strip down the entire image... it was intimidating. Then I was able to reduce the stripping by applying a blocker to bright spots via a Fourier image. I learned that this effect helps the stripes blend in. Out of curiosity, I applied a second Fourier image this time without a LowPass effect and found that it greatly improved the clarity of my image. A second enhancement was done in ArcMap using the Focal Statistics tool which performs statistical tests using adjustable kernel of many shapes and sizes, and also calculates individual values for every cell. In the provided exercise, I tested Mean and Range values. In my final image I applied a Statistic Type Majority with 7Hx7W. The most image enhancing I've done in the past has been in Photoshop. Learning these new image enhancing processes for larger images was very ...

This week we focused on Location-Allocation modeling. This type of modeling is extremely useful in Vehicle Routing Problem (VRP) scenarios and determining the best solutions of distribution for specific areas or market targets. The lab consisted of two parts again. The first being a tutorial portion based on exercises from ESRI's ArcGIS Desktop page. The second required to perform a VRP analysis using Location-Allocation in order to adjust the assignment of market areas serviced by distribution centers throughout the U.S.