GEO599/GEO584-Advanced Spatial Statistics and GIS, 2013-2016

I conducted a hot spot analysis for the cackling goose use of the Willamette Valley. As previously mentioned, I was curious how the geese are using the area throughout the winter season (October – April). I conducted a hot spot analysis for for each month using location points from the entire time series, 1997-2011, to attempt to discern any changes in landscape level use of the valley throughout the winter. All the maps passed the common sense test (the clusters were right over the refuges, no floating hot spots, etc.) which was somewhat heartening.

My first step was separating the data into month files and creating maps of each month in Arc. I also had not seen the data before, so it was nice to pull the points into Arc finally.

Cackling goose flock locations throughout the Willamette Valley, Oregon from 1997-2011.

Secondly I ran hot spot analyses on each month and compared. The hot spots were centered on the four federal refuges in the valley, Finley, Ankney, Baskett, and Sauvie Island (from south to north). Most of the winter months looked more or less the same, except the beginning and end of the winter season.

October vs. November

March vs. April

The addition of two hot spots between October and November, and the loss of one (Sauvie Island) in April likely reflects what sort of agriculture timing the refuges are on, but I am working on exploring why these particular locations are/are not used in those months.

My goal in this course is to create a sort of ‘analytical recipe’ for dealing with the extensive data set I’ve accumulated over the past couple years. I am interested in using the magnetic properties of sediments as proxy for mapping heavy metal concentrations. Magnetic measurements are fast, non-destructive and require little to no preparation. As such, they present a possible replacement for expensive and time consuming geochemical analyses. Less money and less time on monitoring means more money and more time for remediation. Magnetic measurements also yield information that is not acquired in general geochemical approaches such as dominant mineralogy.
The data I will be analyzing consists of measurements for the following properties: magnetic susceptibility (high field, low field and frequency dependence), anhysteric remnant magnetization, isothermal remnant magnetization, various derived ratios and heavy metal concentrations taken using a portable x-ray fluorescence gun. Samples have been taken semi-randomly from the abandoned mine which spans an area of approximately 75km2 and also from nearby streams; Middle Creek and Cow Creek. There is no temporal dimension to the data…yet.
Here are a couple of maps showing sampling points and location of the mine.

Here are a couple pictures of the site showing the encapsulation mound and surrounding area. What a lovely view from such a degraded place.

It will be necessary to correlate various magnetic properties with heavy metal concentrations for different grain sizes. The end product will be statistical relationships that describe the correlation of specific properties with the concentration of various metals. It is highly likely that not all metals of concern can be inferred from magnetic measurements. It will be interesting to see which metals, if any, can be mapped using magnetic approaches. The end map could show concentrations of specific metals mapped as weighted circles for each point that was sampled. For statistically significant correlations, maps could be produced to show how the magnetic measurements relate to concentrations as a side-by-side comparison. Other suggestions are welcome for this.
It is expected that specific metals will associate with various magnetic properties and that the concentration of metals will be highest in areas that are closer to drainage ways and extraction sites.

UPDATE
I have been successful in mapping some of the metals data in ARCMap, however Hot Spot Analysis has been giving me errors. There are obvious hot spots for nickel which is to be expected as nickel is prevalent in this area. The map for copper shows no significant hotspots. There are likely ecological and physical explanations for this lack of copper. It was one of the metals mined at this site.
The first image shows hotspots for nickel and the second one shows copper.

I have included a screen shot of my error message. If anyone has any suggestions, please let me know.

As stated in previous posts, the goal of my project is to explore the difference between point-source temperature recordings at NOAA monitoring stations and the land surface temperature images made available daily from the NASA MODIS satellites. As a first step in comparing these datasets, hot-spot statistics and spatial autocorrelation were used to identify any areas where the difference between the data was significantly non-random. The steps below outline this process.

Data Selection and Cleaning

To begin exploring the data, I selected a single day (January 1, 2015) and linked the measurements from both sources into one shapefile. The NOAA data were downloaded as a CSV file from the NOAA National Climate Data Center (http://www.ncdc.noaa.gov/). This data arrives as a mostly cleaned CSV and the only transformation required was to convert the temperature readings from tenths of a degree Celsius to degrees Celsius. The MODIS raster image was downloaded from the USGS Earth Explorer engine (http://earthexplorer.usgs.gov/) and required some work before it could be used. The raster was re-projected from the unspecified cylindrical projection used for MODIS products to WGS84 to match the point shapefile using the ‘Project Raster’ tool in ArcGIS (http://help.arcgis.com/en/arcgisdesktop/10.0/help/index.html#//00170000007q000000). Once re-projected, the image then had to be re-scaled and converted from Kelvin to Celsius [OutRaster = ((InRaster * 0.02) – 273.16)] before being used.

Joining the Data

Once both data sets were cleaned and ready to be used, the two were joined together for analysis. This was done using a custom Python script, however the ‘Extract Values to Points’ tool in ArcGIS (http://help.arcgis.com/en/arcgisdesktop/10.0/help/index.html#//009z0000002t000000.htm) or QGIS completes the same task. The final shapefile contained a point for each monitoring station with fields for the NOAA and MODIS values along with the difference between the two represented as the absolute value of the MODIS value minus the NOAA value (Difference = |MODIS – NOAA|). This difference figure is what is used for the analysis.

Hot-Spot Map in ArcGIS

The next step was to create a hot-spot map of the difference figures to identify any areas of significantly greater or less difference. This was done using the ‘Hot Spot Analysis’ tool in ArcGIS (http://resources.arcgis.com/en/help/main/10.1/index.html#//005p00000010000000). In the image below, we can see that there is an area of greater difference in central Oregon and an area of less difference near Portland.

It is important to note that neither of these are significant, and most likely represent differences in the terrain. I suspect that the area of higher difference in central Oregon is due to the fact that fluctuations in temperature are much greater there than in the valley. Furthermore, the temperature in the area of less difference is more stable and therefore would not be as prone to error. It is important to remember that the NOAA data represent daily temperature averages, while the MODIS data represent the land surface temperature at the specific time when the image was taken. To explore this idea further, future analysis will include some variable to account for location or land-use type.

Spatial Autocorrelation

Finally, the Moran’s I statistic was calculated to further explore if there is any significant spatial autocorrelation in the difference measurements. This was done using the ‘Spatial Autocorrelation’ tool in ArcGIS (http://resources.arcgis.com/en/help/main/10.1/index.html#//005p0000000n000000). The output is shown below:

This statistic showed that there is no significant spatial autocorrelation. The combination of the very low Moran’s I and the high P-value lead to the conclusion that the difference figures are randomly dispersed throughout the points.

There are some areas in the map that have very high difference on this one day. The next step is to explore these data over a larger time frame to see if the pattern of the difference is the same or different. I plan to download data for at least a few days from each month of 2014 and explore the spatial pattern of the difference between each data set.

The goal of this exercise was to investigate spatial autocorrelation in the movement parameters of a foraging humpback whale. I used location points of sightings of the whale at the water surface between consecutive dives to infer the path of the whale (Friedlaender et al., 2009). To facilitate the analysis, I assumed linear travel of the whale below the surface between consecutive surfacings. I projected the location points and used the adehabitatLT package in R CRAN (Calenge, 2011) to plot the location points of the whale as well as the linear travel segments between these points (see graph below). trajectorymn06_188a

The blue triangle indicates the first, the red square the last observation of the whale at the surface.

Using projected data, the adehabitatLT package calculates the distance traveled by the whale between consecutive observations, the turning angle between consecutive linear segments of the whale’s path as well as the duration between the observations. Using the distance and duration data I calculated the swimming speed of the whale.

Then I calculated the spatial autocorrelation in swimming speed and turning angles using Moran’s I for the entire path, and also for a small section of the path which I assumed to be a foraging area of the whale (cluster of points southeast of the blue triangle). In this small area, the whale spent a comparatively large amount of its time and swam shorter distances between consecutive surfacings, possibly indicating foraging activity.

A small p-value in one of the parameters would provide convincing evidence for the hypothesis that the movement of the whale is autocorrelated in the respective parameter, i.e. that neighboring locations have more similar values than locations that are further apart.

The results from the current analysis (see table below) provide moderate evidence for spatial autocorrelation in swimming speed for the analysis of the entire path, indicating that the whale swam slower in certain parts of its path and faster in other parts (Calenge 2011). However there was no evidence to suggest that travel speed in the small foraging area was autocorrelated. This could be explained by the fact that in the foraging area, the whale swam at a constant, slow speed to the probability of prey detection or encounter (Benhamou, 1992). When leaving this foraging area, the whale is likely to increase its speed, resulting in separate areas of the whale’s path with lower and higher swimming speeds, which would explain the autocorrelation in swimming speed observed for the entire path.

	Entire path		Foraging area
	Speed	Angle	Speed	Angle
p-value	0.017	0.841	0.702	0.651

Benhamou, S. (1992). Efficiency of area-concentrated searching behaviour in a continuous patchy environment. Journal of Theoretical Biology – J THEOR BIOL, 159(1), 67–81. http://doi.org/10.1016/S0022-5193(05)80768-4

Calenge, C. (2011). Analysis of Animal Movements in R: the adehabitatLT Package. Saint Benoist, Auffargis, France: Office Nationale de La Chasse. Retrieved from http://cran.gis-lab.info/web/packages/adehabitatLT/vignettes/adehabitatLT.pdf

Friedlaender, A. S., Hazen, E. L., Nowacek, D. P., Halpin, P. N., Ware, C., Weinrich, M. T., Hurst, T., Wiley, D. (2009). Diel changes in humpback whale Megaptera novaeangliae feeding behavior in response to sand lance Ammodytes spp. behavior and distribution. Marine Ecology Progress Series, 395, 91–100. http://doi.org/10.3354/meps08003

I will (hopefully) be exploring two different data sets in this course. My MS work (described in another post) does not contain any spatially explicit points, so work with programs like Arc becomes more difficult. For several of these analyses I will be using a dataset from the Oregon Department of Fish and Wildlife of cackling goose flock locations.

Cackling geese (Branta hutchinsii minima) are small-bodied geese in the Canada/cackling goose complex (http://www.sibleyguides.com/2007/07/identification-of-cackling-and-canada-goose/). The cackling goose is a is a migratory, Arctic breeding goose with breeding range primarily in the Y-K Delta of Alaska and wintering primarily in Oregon and Washington. Cackling geese are now the most abundant goose species wintering in the Willamette Valley. This follows the general explosion of most Canada goose and cackling goose populations in North America starting around 1966, but the range shift seen in cackling geese is unique coupled with the drastic increase in population size. The cackling goose has increased in number dramatically from an all-time low of less than 20,000 counted in fall surveys during the winter of 1984-85 to over 200,000 birds currently. Along with this population increase has come a change in winter distribution with significantly more use of Oregon and Washington instead of the Central Valley of California, with most birds being found in Oregon’s Willamette Valley and lower Columbia River. To reduce agricultural crop depredation, Oregon refuges have switched to habitat management practices to try and draw cackling geese onto refuge lands. There are also special hunting periods throughout the winter that may influence how flocks use available habitat. Interestingly, cackling geese have consistently been observed increasing their use of urban habitats such as golf courses, parks, sports fields, and residencies.

These birds have been collared on an individual basis since the mid-1990’s. While these data are used largely for mark-recapture analyses to gauge population size, it also produces a large number of flock locations throughout the winter within the Willamette Valley region. Within Oregon, the data spans 1997-2011 and contains 3,141 flock locations with associated latitude/longitude information. These data have been gathered opportunistically by state and federal personnel, and require a bit of data management beforehand.

Using these data, I hope to explore 1) if flock use has changed over the last 20 years, 2) if flock use changes throughout the winter, 3) what habitats feature flocks are most often using (refuge, agricultural, urban), and 4) if flock size changed over time or is influenced by habitat type.

To approach these questions, I would like to conduct several comparable hot spot analyses. I would like to produce a series of maps to provide to ODFW. Understanding flock use is important for resource managers as the population increase pushes birds into private agricultural and urban regions, and if use is changing over time.

As described in my previous blog post, my original intent was to investigate successional patterns of urban re-colonization by diurnal raptors using eBird data. I soon realized that Exercises 2 and 3 would not be possible with point data that I have because the data alone do not have any quantitative attributes that vary in any meaningful way in relation to my research question. After discussing other potential research questions with Julia, we decided that analyzing differing patterns of year-round residency across an urban/rural gradient would be a reasonable alternative that still addresses some of the same overarching themes.

My general approach to conducting this analysis was to calculate the ratio of the number of days a particular species was observed within a given year to the number of days observations of any species were made. This ratio is calculated per cell in a raster covering the extent of the study area. If the ratio is closer to 1 in certain places, then that species of bird is likely staying in that area for more of the year. A value of 0 would indicate that at least one other species was observed, but the species of interest was not observed at all at that location.

I initially thought I would get more meaningful results with a larger sample size so for this initial analysis, I chose to analyze the residency of red-tailed hawks (n = 6,607), one of the most commonly reported species of raptor. The year-round range of red-tailed hawks, however, overlaps with my study site in NW Oregon which might have influenced my results. I therefore repeated the analysis with observations of merlins (n = 589), a species without a year-round range overlapping the study site, to see if patterns in the data were different. Of course, merlins and red-tailed hawks may respond to urban and rural environmental characteristics differently. For both species, I used observations from 2014 only.

An overview of the workflow for this analysis is shown below , but I will also describe each step in detail.

Step 1 Dissolve all observations dataset and species observation dataset by date and observer ID fields.

Step 2 Point Density on both sets of dissolved observations with a cell size of 200 m and a neighborhood of 1 cell

Step 3 Raster Caculator on both points density rasters to multiply each by 200. This produces a raster where each cell is a count of the number of points within that cell. (Not shown in workflow schematic due to space limitations.)

Screen Shot 2015-04-14 at 10.22.32 PM — Observations of all species per 200 m

Screen Shot 2015-04-14 at 10.23.16 PM — Red-tailed hawk observations per 200 m

Step 4 Raster Calculator to compute raster where each cell is a ratio of species observation count to total observation count

Step 5 Extract Values to Points with all observation dataset as input points and ratio raster as values to sample.

Step 6 Getis-Ord Gi* Hotspot analysis on sampled points with Fixed Distance Band as the Conceptualization of Spatial Relationships parameter.

Screen Shot 2015-04-14 at 11.21.53 PM — Hot and cold spots of red-tailed hawk residency. Black polygons are 2010 Census delineated urbanized areas.

While the results seem to reflect a pattern I would expect, I’m not sure that I trust them entirely. This is for several reasons:

The merlin hotspot map is very similar despite the fact that the observations were much more sparse than red-tailed hawk observations. The merlin map also shows hotspots in locations where there aren’t any merlin observations too, and the sampled value of many of the points is 0 (meaning other birds were observed but no merlins were).

Screen Shot 2015-04-14 at 11.37.19 PM — Merlin hotspots

I conducted the same sampling procedures using Extract Values to Points but with a grid of points even spaced 1km apart. The hotspot map is very different and seems to be mostly noise. There is only faint evidence of a discernible pattern.

Screen Shot 2015-04-14 at 11.42.56 PM — Hot spot analysis on a 1 km grid of sampled red-tailed hawk residency ratio

There are some data quality issues that I did not address here. For instance, some observers may have only been looking for certain species and might not have reported the species of interest even if it were present. The converse could be true as well. This is usually reported with those kinds of observations but I didn’t filter these out.
Also, the data are very clustered to begin with and I may not have selected the right Conceptualization of Spatial Relationships for the data.

The Eastern North Pacific is a species rich area. A total of 30 marine mammals are known to occur in Oregon and Washington waters. However, the seasonal abundance and distribution of marine mammals in Oregon’s near shore waters is not well understood. The goals of my project are to use passive acoustic monitoring, visual line transects and oceanographic data collection in Newport, Oregon’s near shore waters to [A] study the temporal distribution, spatiotemporal scales of occurrence and movement patterns of marine mammals; [B] study physical, chemical and lower-trophic-level ecological drivers of these occurrence patterns, producing a quantitative model of occurrence; I am going to specifically concentrate on harbor porpoises as an indicator species. Harbor porpoises are of elevated concern because of their high sensitivity to anthropogenic noise, such as wave energy converters.

harbor-porpoises_569_600x450 — Photo by National Geographic

Since October of 2013, I have been using two methodologies with high spatial and temporal resolution – combined passive-acoustic and visual surveys – to effectively monitor porpoises in Newport, Oregon’s near shore waters. In addition, physical, chemical, and biological oceanographic data has been collected in-situ with during the duration of these survey methodologies. At this point, I have survey data from about 18 months of transect surveys. Data for this analysis were collected from multiple surveys at the Pacific Marine Energy Center (PMEC) North (NETS) and South (SETS) Energy Test Sites and the Newport Hydrographic Line (NH).Both Nets and Sets are near shore (within 5 miles), The NH line extends west from Newport for 25 miles, and all three are subject to up and downwelling events ubiquitous to the Oregon Coast.

10511604_10152778085291070_5111035247949699751_o — Photo by Alex Turpin

There has previously been a lack of mammal distribution data in the area and therefore no reference to the data set, I have collected and created. It is expected that I will find trends with near shore distributions (NETS and SETS sights) that change seasonally with upwelling and downwelling events. Similar distribution changes are expected along our offshore NH line.

Using established statistical analysis including environmental variable mapping and spatial interpolation methods, I am hoping to quantify correlations between harbor porpoise movement patters and distributions with biophysical variables. I would also like to identify and quantify harbor porpoise “hang-outs” with habitat- association models.

Porpoise-1-300x169 — Photo by Amanda Holdman

During the course, I would like to first map my sighting data over a bathymetry layer in ArcGIS to see a visual representation of my collected data so far. I would like to separate my sightings by harbor porpoise and other, to immediately hope to see a trend (without statistics) of harbor porpoises being more distributed near shore than off shore since they are known to congregate on shelf breaks in less than 200 meters of water. Next, I would like to spatial interpolation methods to bin my oceanographic data to create a “fluid” picture of Newport, Oregon Oceanography. Finally, I will look for trends (and use spatial statistics) to determine what biological, physical, chemical, and lower-trophic levels drive the occurrence of porpoises. Determining what factors affect distributions can be incorporated into a habitat model to help predict when and where harbor porpoises may be in the future.

The analysis of this data set will provide needed information on harbor porpoise occurrence and behavior and an understanding of the physical and biological factors leading to these occurrences to serve as a baseline measure of mammal hot spots. Results from this study will generate data and information that can be used to answer key regulatory and impact – mitigation questions for renewable energy siting and permitting.

As far as my experience goes to do all of this, I have taken introductory classes on the statistical package R and ArcGIS, which included a couple of model builder lessons, but have never worked with python. This is really my first chance to break down my excel spreadsheet of data and upgrade into a visual representation and understanding of distributions and movements of harbor porpoises across space and time. I am looking forward to advancing my skills, struggling through the 5 stages of grief, and working with my classmates. Here’s to learning!

This week I began exploring the tools and techniques I proposed using in “My Spatial Problem” blog. The goal of my project is to investigate the foraging ranges of Adelie and Gentoo penguins over the course of a breeding season on the Western Antarctic Peninsula. Specifically, I’d like to calculate the total area each species utilizes for foraging, identify core foraging areas, and calculate the percent overlap between the ranges of these two species.

I began this process by importing XY Data (latitudinal and longitudinal coordinates in decimal degrees) into ArcMap. To start I’ve randomly chosen data from three Adelie and three Gentoo penguins, representing at-sea areas where PTT tags were able to successfully transmit location data to a satellite.

I utilized an online database called the Antarctic Digital Database (©1993-2015 Scientific Committee on Antarctic Research) to obtain basemaps for general orientation and reference. They aren’t perfect, but useful enough to show the general location of each species’ breeding colony, and the location of a nearby marine canyon that may be of ecological importance. I found these basemaps to be important right away. They aren’t directly necessary for spatial analysis, but they are critical in terms of initially assessing the space these species are utilizing and determining whether things make sense! I discovered two important things.

In my first blog post, I briefly glossed over the importance of filtering these datapoints to eliminate poor quality data. Each datapoint (downloaded from ARGOS) comes with an associated estimation of accuracy. I decided to initially skip this step while I use this data to practice spatial analysis in Arc. This explains why some datapoints are on land, and it might explain outliers, like those seen in the Neumayer Channel (unlabeled, but at right in Figure 1 and 2 below).
Upon visually inspecting these datapoints I realized there must be something wrong, because my initial map of Gentoo foraging locations showed a lot of clustering around Torgersen Island (Figure 1). Torgersen is the location of the Adelie colony, and Biscoe is the location of the Gentoo colony. It was unusual that the points of two individuals did not originate from Biscoe. In fact, the original data were from Adelie’s and were mixed up in the original datasource. This is corrected in Figure 2.

Figure 1. Erroneous Gentoo locations originating from Torgersen Island

Figure 2. Corrected Gentoo locations originating from Biscoe Island

Next, I began to research kernel density estimation techniques. Much of the literature I’ve read where similar techniques have been used has alluded to kernel density estimation techniques, percent volume contours, and, a spatial analyst extension called Animal Movement. I was dismayed to find out that the Animal Movement Extension is no longer in commission and not available for Arc 9 and 10. The next extension/software I researched that provided these tools was called Hawths tools, and is also discontinued. Its replacement is called Geospatial Modeling Environment (http://www.spatialecology.com/gme/). I am still considering using this software, however it would require learning/using an entirely different program.

While considering these things, I attempted to search for tools in the ArcToolbox that might be useful. I used the Kernel Density tool to create kernel density layers for each species. I combined the individual datapoints from each Adelie into one layer (Fig 3), and the three Gentoo individuals into another (Fig 4), and then calculated separate kernel density layers for each. It was encouraging to find that the output of this was a pretty good visual representation of “hotspots”, however, I’ve since been stuck attempting to understand exactly what Arc did here. Specifically, I don’t understand the values that are associated with each contour.

Figure 3. Combined Adelie locations with kernel density layer, note grid-like structure of the points in the center

Figure 4. Combined Gentoo locations with kernel density layer and legend at left describing kernel density values for Fig 3 and 4

If I can figure this out I will be able to determine whether this tool will work for the purpose of my project. I’d like to determine the area within 50% and 95% contour lines. To do this I need to accurately create these contours, and this will require more knowledge about how the kernel density tool works. So far I’ve experimented changing different things associated with “Classification” in the Symbology tab of Layer Properties. Break Values seem to determine each contour, and there is an option to change these values. There is an option to specify %, but the units/area calculated by the % values do not seem right (Figure 5). The legend contains the values associated with 25, 50 and 95% breakpoints (0-25, 25-50, 50-95). I will continue to explore this function, as well as the Geospatial Modeling Environment program described above.

Looking closely at the Adelie datapoints (Fig 3) it appears that they are way too grid-like. It turns out that the original XY data (decimal degrees) is only to four decimal places. Eventually I will need to return to the original datasource for more fine-scale points (hopefully they exist).

My next steps include deciphering the kernel density output, and learning how changing factors such as grid cell size and search radius affect kernel density calculations. After that I will need to determine which tool/calculation will allow me to compute % overlap between the two species ranges.

Figure 5. Adelie locations with a kernel density layer where breakpoints were manually entered, pink is supposed to represent 25-50% and blue is supposed to represent 50-75%?

Analysis of flood levels at 10, 50, 100 and 500 year events and their impact on potable, waste, and storm water systems in Alsea, Oregon, USA.

The objective of this research is to:

Investigate whether the existing Geographic Information Systems (GIS) information is complete, or needs to have more data added to represent the current condition of the water systems.
Work with other individuals to update the GIS information so that aspects of the systems which are lacking can be repaired.
Determine the cause of the sinkhole that has developed next to the Alsea School District’s playground, and under the sidewalk that accesses the school grounds along the western edge next to Route 34.
Survey the historical flood effects and where the impacts would be detrimental to the town.
Create boundaries of the 10, 50, 100 and 500 year floods to determine properties and systems potentially impacted.
Develop plans to help the Alsea Emergency Preparedness Committee visualize the areas of greatest concern.
Distribute the findings in the Alsea Valley Voice. Upon final grading and review by the Geographic Information Systems & Science (GIScience) Department at Oregon State University (OSU).

The Alsea Watershed and Benton County Department of Public Works:

Most studies investigating the spatial scale of the watershed have focused on the town and the local roads. Multiple types of storm-drain pipes from concrete to poly (vinyl chloride), also called PVC, have been used over the years. Connecting to these makes repairs difficult. The alternative is to replace the entire line.

Analyzing what is needed to ensure proper water distribution and drainage will contribute to the town’s knowledge of where the water will travel, and how the storm water system may conflict with the existing man-made pathways.

The existing data for this analysis is sourced from public records through Doug Sachinger, the GIS Coordinator from Benton County Public Works. He provided all of Benton County’s existing data for Alsea. This is because the town of Alsea does not have a public works department, and is unincorporated. GIS data was missing throughout the town. Stub-outs for water meters appear on the map without any connecting system to feed them, and no water meter data exists yet to verify that any particular stub out is providing potable water to a location.

Alsea

Alsea Potable, Sewer and Storm Water Systems

This is an example of the incomplete GIS data that the town of Alsea currently has on file with Benton County. There are stub-out locations that do not appear to be connected to the potable water system, and houses that are currently being serviced by the public water system which do not have their stub-outs shown.

stubouts

Alsea Stub out not connected to the water system

The size of homes and the tax evaluation to run a hot spot analysis were shaded darker red, indicating the property locations made the lots more valuable.

regionalhotspot

Regional Hotspot data

It is interesting to note that the areas of highest value are located between the inlet of the potable water from the Alsea River and the outlet of the waste water treatment facility downstream into the Alsea River.

HotspotAlsea

Alsea Hot Spot for property values

EXISTING SYSTEMS IN ALSEA

Since the town of Alsea is an unincorporated township, Benton County is the authority on Public Works. In March, 2015 the old town storm water overflow was permeating into town members’ crawlspaces. To remedy this, the county added three surface catchments and rerouted the water to a storm water ditch. This added to the volume of water exiting through the school’s playground.

Temporary Stabilization Concern

When evaluating the potential cause of the sinkhole, water does not constantly flow from the opening, so it is less likely to be from a potable water line break. When there is an active rain/storm water event or soon after, water floods the playground area and travels into the school’s paved pick up and drop off area.

sinkholepond

Sinkhole pond that develops when it rains

Alsea’s current water systems need additional mapping. Sadi Stouder, a Civil Engineering graduate student contacted me. She will be adding the necessary information, and is interested is using my on-site assistance. The objective is to gather the additional GIS data. Bob Miller, from Benton County Public Works, records the town’s water meter usage, and may be willing to provide meter base locations. This will help to document stub out locations and meter bases locations in order to connect them on GIS to the potable water system.

The raster file is now at 10 foot contour intervals. This will help determine where the water will travel through town.

10ftcontour

10ft Contour Interval

sinkholecontour

10ft Contour at the sinkhole by the Alsea School

Facing North toward R

Close-up where gravel and a metal panel have been added for safety

Facing South toward Alsechool and pathway of the storm water drainage pipe

Permanent Stabilization Practices

A few examples of permanent stabilization of areas with open soil profiles are buildings, paved areas and seeding of all non paved areas. Surface roughening, planting and re-vegetation have decreased erosion around the Alsea School District (Pre-K through 12^th grade). Individuals are functioning as Storm Water Pollution Prevention (SWPP) leaders in the community. The horticulture group is also working on gardens and selling plants to raise money. Erosion control works hand in hand with reducing the impacts of flood waters in small communities.

Land use will reduce the effects of flooding and the weather conditions that occur at the same time. By planting trees as clusters or groups, the wind is less likely to topple over a few vs. just one.

Flood of 2rains that began overnight Tuesday and poured steadily on Wednesday triggered floods throughout western Oregon on Thursday, with high water

Quote:

http://www.gazettetimes.com/news/local/the-flood-of/article_96c74746-4339-11e1-be3a-0019bb2963f4.html

January 20, 2012 5:00 am Corvallis Gazette-Times

“Waters rise: Floodwaters posed a threat throughout Benton County on Thursday as the Marys River reached a record flood stage of 21.41 feet. Not as bad as 1996, but a strong reminder. Heavy rains that began overnight Tuesday and poured steadily on Wednesday triggered floods throughout western Oregon on Thursday, with high water pouring over roads throughout Benton County and soggy ground helping to create landslides, downed trees and other weather mayhem….Area schools and universities were taking no chances, though: Oregon State University’s Corvallis campus will be closed today, as will schools in the Corvallis School District and Alsea… In the 24 hours ending at 8 a.m. Thursday, 4.02 inches of rain fell at Hyslop Farm outside of Corvallis – easily breaking the previous record for Jan. 19, 2.25 inches, a mark set in 1911. In fact, Wednesday went into the record books as the third-rainiest day in 101 years, said Kathie Dello of the Oregon Climate Service at OSU. The only other days that saw more than 4 inches of rain in 24 hours were Nov. 19, 1996, when 4.45 inches fell, triggering massive flooding that cut off access to south Corvallis for several days, and Jan. 28, 1965, when 4.28 inches fell. The effect of the rain was most evident at the Marys River. It reached 21.41 feet Thursday morning, breaking the old record of 20.9 feet, and flooded parts of Corvallis and Philomath…. The Alsea School District closed schools and its playing fields and some buildings were flooded. ”

The quote above explains the history of flooding in the Willamette Valley and Coastal Range. The next flood event may be in 10 years or it might be the 500 year event but the true extent of such an event is unknown as the data has not yet been recorded for 500 years.

The Federal Emergency Management Agency (FEMA) website allows public access to download data associated with flooding. This information is not in GIS format or even in a data table to convert. The process then was to find the Alsea River locations that were used as reference points and determine the elevation of the flood plains.

The categories are:

2 % likelihood i.e. not very often (500yrs)
1% more often (100yrs)
2% chance of flood is within a lifetime (50 yrs)
10% probability of flood levels every (10yrs)r (Federal Emergency Management Agency)

Even though it seems odd that FEMA is working on a document that is called Flood Insurance Study, maybe it had the people’s best interests at heart.

The purpose is to examine the real vs. perceived risks of flood events and determine the actual differences between a 10 year flood and a 500 year flood.O

regon

Special Flood Hazard Area

There is only one map for the 10, 50, 100 and 500 year events. This map looks like the 10yr flood event.

The elevations on the graph provide the values of flood plains during each of the 10, 50, 100 and 500 year events.

A number of controls have been put into place by the Emergency Preparedness Group in Alsea. One control is lift stations, or pump stations. These help ensure that high surface water levels don’t cause wastewater or storm water to back flow into people’s homes.

Additionally, litter, debris, and construction chemicals that could be exposed to storm water should be prevented from becoming a pollutant source in storm water discharges. (10) Year Flood Event

accrossTen Year Flood Event Extent

The 10 year flood event appears in Alsea as:

Ten Year Flood Event in AlseaFifty Year Flood Event Extent

The 50 year flood event appears in Alsea as:

The 100 year flood event appears in Alsea as: 100yrRegional

The 500 year flood event appears in Alsea as:

The overlays from the Benton County Public Works GIS Department show Alsea potable, waste and storm water systems, yet did not have the ArcGIS flood data. This next level of research was found through the FEMA website and adapted into the existing map. Once the flood data could be viewed with the existing layers, flood elevations were determined and placed into Table 2.

The base map was then edited through the toolbox within the customize tab. When selected, the locations A-E were added as reference points. Select the contour lines available. If 10 foot then convert to 2 ft so it will provide the results with better quality results and details. rocedural ArcMap GIS example in Alsea

When we think of floods, the worst comes to mind. Many in this are have known the effects first hand. This report is for those that haven’t experienced the high waters and to recognize the areas of concern. This will allow the development of other methods to reduce the negative impacts on our community, and our personal items in our basement.

The map below shows the 10 year flood in blue and then the additional areas affected by the 50 year flood in pink, the 100 year flood in purple, and then the 500 year flood in greenish-yellow. If it’s in your home, the couple foot difference is not minimal, but in comparison the changes in area are slight. These are also estimated and could be higher or lower depending on rain, snow loads in the mountains, water table, vegetation and many other factors.

Regional 10, 50,100 and 500 year Flood Extents

Alsea 10, 50,100 and 500 year Flood Extents

REFERENCES

Federal Emergency Management Agency:

https://msc.fema.gov/portal/search?AddressQuery=alsea%2C%20oregon

US Department of Fish and Wildlife – www.fws.gov

Doug Sachinger, GIS Coordinator, Benton County, Oregon, Public Works

(Staff Reporter) 2012,January 20, “The Flood” Corvallis Gazette-Times

http://heresmeme.com/AVV/Alsea%20Flood%202012/New/page_01.htm

My research objective is to investigate two spatial aspects of humpback whale (Megaptera novaeangliae) surface feeding in and around the Stellwagen Bank National Marine Sanctuary in the southern Gulf of Maine, USA. Specifically, I aim to:

Quantify the spatial scale of this behavior.
Investigate whether this behavior is more frequently observed in certain areas of the study region.

Most studies investigating the spatial scale of humpback whale movement have focused on large spatial and temporal scales, using 1-2 location points per day over the course of several days or weeks during which the animals traveled hundreds or thousands of kilometers (Dalla Rosa et al. 2008; Heide-Jorgensen and Laidre 2007; Kennedy et al. 2013). In contrast, the proposed study aims at investigating the detailed movement of these whales on the temporal and spatial scales of daily bouts of foraging events. Analyzing such fine-scale foraging movement patterns can contribute to our knowledge of how marine predators search for patchily distributed resources (Levin 1992; Pinaud and Weimerskirch 2005).

The existing data for this analysis stems from a long-term study investigating humpback whale behavior and ecology in the southern Gulf of Maine, USA, (for more details, see Friedlaender et al. 2009). Almost every summer since 2004, whales were equipped with non-invasive tags that recorded detailed information on the underwater movement of the whales or collected video-footage of the behavior of the tagged animal and associated whales. During daylight deployments that usually lasted for up to 8 hours, focal follows were conducted from a small boat following the tagged whale, during which detailed information on the behavior of the tagged whale at the water surface was collected. Because the tags did not contain a GPS, range and bearing information on the whale were also collected at least once when the whale was observed at the surface in between consecutive dives, usually resulting in the collection of one location point every 3-5 min. Continuous GPS locations of the boat were automatically collected. Based on the time stamps of the range/bearing data and the boat GPS data, the GPS location collected at or close to the time the range and bearing data was collected, were identified and together this data was used to calculate the location of the whale.

The analysis proposed here will use the whale behavioral observation data to identify focal follows during which surface feeding was observed. The location data of these focal follows will be used for the following analysis. For each focal follow of a surface feeding whale, I will use the R package adehabitat (Calenge 2011) to implement first-passage time analysis to calculate the spatial scale of surface feeding, and to identify areas of intense foraging effort. The following description of the method is based on Fauchald & Tveraa (2003). First-passage time is a metric used to quantify search effort along an animal movement path. Around each location point, a circle of a given radius is created, and the amount of time the animal spent within the area of the circle is measured. The measurement is then repeated for each location point of the animal’s path with successively increasing circle radii. Increasing the radii will increase the amount of time the animal spent within the circle, but the increase in time will be greater in areas of intense search effort compared to areas through which the animal was simply traveling. The radius at which the variance in first-passage time between the different location points is greatest represents the spatial scale of foraging effort. I intend to statistically test for differences in the spatial scales of surface feeding between individuals and as a function of group size. At the radius representing the spatial scale of foraging, those circles with the longest first-passage times identify areas where foraging effort is concentrated (Bailey & Thompson 2006). Comparing the locations of intense foraging effort between individuals, areas within the study region can be identified that represent suitable foraging habitat (Bailey & Thompson 2006). The expected outcome of this part of the analysis is a map of the study region displaying locations of suitable foraging habitat based on first-passage time calculation.

I expect to find that different individuals have a similar spatial scale of surface feeding, as I anticipate that the spatial scale of foraging is correlated with spatial metrics of prey schools (Benoit-Bird et al. 2013). Because the spatial scale of search effort is likely to be larger than the spatial scale of the prey patches themselves, I expect the spatial scale of foraging for all individuals to be larger than the average prey school length in the area, which is ca. 139 m (Hazen et al. 2009). I expect to find a positive correlation between the spatial scale of surface feeding and group size because I anticipate that larger groups will cover a wider area during their search. I expect to see a concentration of surface feeding locations in the western part of the sanctuary region as this has been found to be an important feeding area in a previous study using a subset of the data I am planning on analyzing here (Hazen et al. 2009). This is due to the substrate type, topography and oceanographic conditions in this area which serve to attract and aggregate prey (Hazen et al. 2009).

I currently have basic knowledge of ArcMap and R and no experience with Python or Modelbuilder.

Literature cited:

Bailey, H. & P. Thompson. 2006. “Quantitative Analysis of Bottlenose Dolphin Movement Patterns and Their Relationship with Foraging: Movement Patterns and Foraging.” Journal of Animal Ecology 75 (2): 456–65. doi:10.1111/j.1365-2656.2006.01066.x.

Benoit-Bird, K.J., B.C. Battaile, C.A. Nordstrom, and A.W. Trites. 2013. “Foraging Behavior of Northern Fur Seals Closely Matches the Hierarchical Patch Scales of Prey.” Marine Ecology Progress Series 479 (April): 283–302. doi:10.3354/meps10209.

Calenge, C. 2011. “Analysis of Animal Movements in R: The adehabitatLT Package.” Saint Benoist, Auffargis, France: Office Nationale de La Chasse. http://cran.gis-lab.info/web/packages/adehabitatLT/vignettes/adehabitatLT.pdf.

Dalla Rosa, L., E. R. Secchi, Y. G. Maia, A. N. Zerbini, and M. P. Heide-Jørgensen. 2008. “Movements of Satellite-Monitored Humpback Whales on Their Feeding Ground along the Antarctic Peninsula.” Polar Biology 31 (7): 771–81. doi:10.1007/s00300-008-0415-2.

Fauchald, P. & T. Tveraa. 2003. “Using First-Passage Time in the Analysis of Area-Restricted Search and Habitat Selection.” Ecology 84 (2): 282–88.

Friedlaender, A.S., E.L. Hazen, D.P. Nowacek, P.N. Halpin, C. Ware, M.T. Weinrich, T. Hurst, and D. Wiley. 2009. “Diel Changes in Humpback Whale Megaptera Novaeangliae Feeding Behavior in Response to Sand Lance Ammodytes Spp. Behavior and Distribution.” Marine Ecology Progress Series 395 (December): 91–100. doi:10.3354/meps08003.

Hazen, E.L., A.S. Friedlaender, M.A. Thompson, C.R. Ware, M.T. Weinrich, P.N. Halpin, and D.N. Wiley. 2009. “Fine-Scale Prey Aggregations and Foraging Ecology of Humpback Whales Megaptera Novaeangliae.” Marine Ecology Progress Series 395 (December): 75–89. doi:10.3354/meps08108.

Heide-Jorgensen, M. P., and K. L. Laidre. 2007. “Autumn Space-Use Patterns of Humpback Whales (Megaptera Novaeangliae) in West Greenland.” Journal of Cetacean Research and Management 9 (2): 121.

Kennedy, A.S., A.N. Zerbini, O.V. Vásquez, N. Gandilhon, P.J. Clapham, and O. Adam. 2013. “Local and Migratory Movements of Humpback Whales (Megaptera Novaeangliae) Satellite-Tracked in the North Atlantic Ocean.” Canadian Journal of Zoology 92 (1): 9–18. doi:10.1139/cjz-2013-0161.

Levin, S.A. 1992. “The Problem of Pattern and Scale in Ecology: The Robert H. MacArthur Award Lecture.” Ecology 73 (6): 1943. doi:10.2307/1941447.

Pinaud, D.D. & H. Weimerskirch. 2005. “Scale-Dependent Habitat Use in a Long-Ranging Central Place Predator.” Journal of Animal Ecology 74 (5): 852–63. doi:10.1111/j.1365-2656.2005.00984.x.

Just another blogs.oregonstate.edu site

Cackling Goose Hot Spot Analysis

Heavy Metal Hotspots at Formosa Superfund Site

NASA vs. NOAA Part I: Using Hot-Spot Statistics and Spatial Autocorrelation to Measure Differences in Temperature Readings

Analyzing spatial autocorrelation in humpback whale foraging movement parameters

Cackling Goose Use of the Willamette Valley

Red-tailed hawk residency in Northwest Oregon

Harbor Porpoise Distribution Patterns off the Coast of Oregon

Investigating the extent of foraging range overlap between Adelie and Gentoo penguins at Palmer Station, Antarctica (week two update).

Analysis of flood levels at 10, 50, 100 and 500 year events on potable, waste, and storm water systems in Alsea, Oregon, USA.

Analysis of the spatial scale and locations of humpback whale surface feeding sites in the southern Gulf of Maine, USA, using first-passage time

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