Spatial analysis and sptial models....Spatial data sources, data reduction methods & spatial analysis Read everything before doing anything! 2 1. Describe in words where is the CCW

Spatial data sources, data reduction methods & spatial analysis

Read everything before doing anything! 1

As Native Americans and European explorers ventured through “Penn’s Woods,” they used its creeks, rivers, and lakes as routes for extracting natural resources. Conodoguinet Creek was one of those routes.

The name ‘Conodoguinet Creek’ is borrowed from Native American words meaning “A Long Way with Many Bends” (Figure 1). The creek itself flows from high on the Kittatinny Ridge, down into and through the fertile Cumberland Valley, and out into the Susquehanna River near Harrisburg, at West Fairview. Entrepreneurs developed water mills (Figure 2) and factories along its banks. The Harrisburg Nail Works for example, built at the mouth, was once the largest manufacturer of nails in the US.

Conodoguinet Creek (hereafter CC) is not the commercial extraction route it once was; railroads supplanted the waterways in the 1800s, US Route 11 was developed in the 1920s, and the Interstate system was built in the 1960s. Today, CC supplies Harrisburg’s western suburbs and communities upstream with drinking water, recreational opportunities, and other ecosystem services.

The Conodoguinet Creek Watershed Association (CCWA) is a non-profit group of environmentally concerned citizens. They act on matters that affect the welfare of the creek and its watershed. The CCWA sees a clear need to conduct periodic inventories of the land, water, and human resources in the watershed so that they can better inform the municipal comprehensive plans and storm water management plans that are being developed.

Our purpose is to conduct an inventory of the human and natural resources in the CC watershed and to present the results in a meaningful way that can help citizens and planners develop comprehensive plans and storm water management plans for the creek and the watershed. We’ll start by answering a few simple questions:

Figure 1. The bendy Conodoguinet Creek is under increasing pressure from a growing population and different opinions about how it should be used.

Figure 2. Heishman’s Mill, built in 1805 along the Conodoguinet Creek, is one of the best preserved mills in the region and serves as a reminder of the 140 other mills that served the region during its agricultural heyday.

Figure 3. The CCWA organizes several stream clean-up events every year. Shown here are three volunteers hauling material dumped illegally.



1. Describe in words where is the CCW is located; how it is situated in both Pennsylvania and the Susquehanna River Basin (see Elrod, 2014; Rodrigue, 2013).

2. How big is the CCW (sq.mi, sq.km; each to 2 decimal places)?

3. How long is the CC (mi, km) and what is the total length of all of its tributaries (mi, km; each to 2 decimal places)?

4. Who are the state-level politicians elected to represent people in the CCW?

5. Which municipalities, by county, are responsible for providing public services to people in the CCW?

a. Which of those municipalities have zoning ordinances?

6. What have been the biggest employment changes in the CCW since 2006?

7. How much CCW area (sq.mi, sq.km, percent of total area) is covered by different land covers?

c. From a bird’s perspective, how much of the watershed is covered by trees or forest?

d. From a dog’s perspective, how much of the watershed is covered by impervious surfaces?

8. Which bedrock lithologies underlie the CCW (sq.mi, sq.km, percent of total area)?

a. Build a map that shows AND build a paragraph that describes:

i. the spatial distribution of bedrock, by lithology, in the CCW

ii. the spatial distribution of the CC and its tributaries with respect to the spatial distribution of bedrock lithology in the CCW

b. Which bedrock lithologies underlie the CC and its tributaries (mi, km, percent of total length)?

CC = Conodoguinet Creek; CCW = Conodoguinet Creek Watershed; CCWT = Conodoguinet Creek Water Trail

Bolstad, 2016: pages 297-298, 303-310, and 314-315

1. Assemble trusted data that are suitable for answering questions about land, water, and human resources in the CCW (Table 1); and

2. Use a spatial reference system that is suitable for accurately calculating stream lengths and watershed areas;

3. Perform appropriate types of spatial analysis to meet your remaining objectives.



Table 1: Download these datasets from their respective distributors (the online PDF version of this document contains hyperlinks). Note that data originators and data distributors are not always the same. Title Data provider Data distributor

Download by direct link to the NHDPlus HR dataset by 4-digit Hydrologic Unit (HU4) - 0205

USGS The National Map

“OnTheMap” data for 2017 and 2007 US Census Bureau US Census Bureau

Bedrock Geology of Pennsylvania PA DCNR PA DCNR

PA municipality boundaries Penn DOT PASDA

PA (state) senatorial boundaries Penn DOT PASDA

PA state house district boundaries Penn DOT PASDA

High-Resolution Land Cover, Commonwealth of Pennsylvania, Chesapeake Bay Watershed and Delaware River Basin, 2013

University of Vermont Spatial Analysis Laboratory

GIS3 website

It is important for you to see the array of available data, so I’m tasking you with gathering and processing several datasets in their native formats. Good organization and patience will be your keys to success, so work intentionally, rename computer-friendly file names with user-friendly file names, and take notes as you create or delete data. About the High-Resolution Land Cover Dataset: UVM’s High-Resolution Land Cover dataset is a monster (818 MB zipped; 18 GB unzipped); what we call “big data.” You want a strong and dependable internet connection to download it. You want to avoid geoprocessing it while on a thumb drive (read/write speeds on thumb drives are typically much slower than on your local hard drive).

Land cover categories in this dataset are represented by 4-bit integer codes. The meaning of each code is given in both the metadata description and the raster attribute table (integer rasters can have attribute tables).

Feel free to delete the ZIP archive after you’ve extracted its contents. Feel free to delete the original “big” raster after saving the small piece you need into your geodatabase. Think carefully before doing any geoprocessing with it.

About the NHDPlus HR: The high resolution National Hydrography “Plus” Dataset (NHDPlus HR) represents the water drainage network of the United States. Vector features represent flowlines, waterbodies, coastlines, dams, and stream gages – all parts of our National Map. While not as big as the land cover raster, you will still want to reduce the NHDPlus HR into manageable pieces. See the details below about moving only what you need into your geodatabase.

You might recall from GIS2 that ArcGIS will automatically add and calculate Shape_Length and Shape_Area fields whenever you store line or polygon features in geodatabase format (but not in a shapefile format). For this lab, I strongly suggest deleting (or turning off) any of the legacy area or length fields that come with the raw data. Rely solely on your new Shape_Length and Shape_Area values. The (NAD83) SP PA South coordinate system ensures that you’ll calculate shape properties with minimal distortions in southern Pennsylvania. Report numeric length [ mi, km ] and area [ sq.mi, sq.km ] values to the nearest hundredths place.

https://www.usgs.gov/core-science-systems/ngp/national-hydrography/access-national-hydrography-products

http://onthemap.ces.census.gov/


https://www.dcnr.pa.gov/Geology/PublicationsAndData/Pages/default.aspx

http://www.pasda.psu.edu/



https://webspace.ship.edu/sadrzy/geo420/index.html



Bolstad, 2016: p373-375, 394-395, and 405-419

Use ArcGIS Pro / Catalog to start and prepare a project. Connect the project to your GIS3/Labs folder. Create

a new project called CCW2019_<your initials>. If you need to, connect to the folder that contains all the raw data you downloaded (Table 1).

Use Catalog to get familiar each raw dataset. Preview the geographies. Preview the attribute tables. And

consult the metadata description of each. Metadata often contains valuable information. You might,

however, have to change your Project > Options > Metadata > Metadata Style from the default “Item Description” style to the more revealing “FGDC Metadata” style to see all your metadata.

Use Create Feature Dataset to create a feature dataset called INVENTORY. A feature dataset is used to hold a collection of feature classes that share a common spatial reference system. When prompted, assign the

metric (NAD83) State Plane PA South coordinate system. All vectors imported into your feature dataset will be re-projected automatically. Shape_Lengths and Shape_Areas will be recalculated automatically.

Next, Select the entire HYDROGRAPHY/NHDFLOWLINE feature class and save it into your inventory dataset. Compare your input and your output geographies; compare your input and output shape attributes. You should notice that the output shapes were re-projected automatically.

Next, Select the WBD/WBDHU10 feature class into your inventory dataset, but this time use the expression option to import the trio of HUC10 polygons associated with Upper, Middle, and Lower Conodoguinet Creek.

Next, use one of the spatial overlay tools in Table 3 to create one seamless watershed polygon that represents the entire Conodoguinet Creek watershed area. Preview your results.

Next, use another data reduction tools in Table 2 to reduce the extent of your NHDFLOWLINE feature class so that it includes only those flowlines inside your Conodoguinet Creek watershed area polygon.

0. Use ArcGIS ModelBuilder to build a model that illustrates the sequence of five GIS operations highlightedabove. Rename inputs, tools, or outputs to make them read-friendly. Unfortunately, you’ll need to useyour favorite screencapture software (“Snipping Tool” in Windows) to get a graphic into your report.

You now have the data you need to answer Questions #1, #2 and #3. 1. Describe in words where is the CCW is located; how it is situated in both Pennsylvania and the

Susquehanna River Basin (see Elrod, 2014; Rodrigue, 2013).

2. How big is the CCW (sq.mi, sq.km; each to 1 decimal place)?

3. How long is the CC (mi, km) and what is the total length of the creek and all of its tributaries (mi, km; eachto 1 decimal place)?

###########

4. Who are the state-level politicians elected to represent people in the CCW?

5. Which municipalities, by county, are responsible for providing public services to people in the CCW?

https://pro.arcgis.com/en/pro-app/help/data/feature-datasets/feature-datasets-in-arcgis-pro.htm



Bolstad, 2016: 378-384

Next, make a layer by adding your dissolved CCW polygon to a new map. Your new map will inherit the spatial reference system of the first spatial dataset you add to it.

Next, make only the layers needed to answer questions 4 and 5. Use Select by Location to find your answers.

###########

6. What have been the biggest employment changes in the CCW since 2007?

Use the US Census Bureau’s ONTHEMAP application to collect jobs data. We’re going to collect two (2) sets of jobs data: 1) for the entire State of Pennsylvania and 2) just for the CCW.

A. Start by using either the Chrome or the Firefox browser (Internet Explorer has trouble with this application) and follow the URL above.

B. Search among States for Pennsylvania; click the result so the map will auto-pan and zoom to Pennsylvania.

C. Find the pop-up balloon and follow the link to Perform Analysis on the Selection Area:

D. Customize your Analysis Settings:

a. Home/Work Area: Work

b. Analysis Type: Area profile with the Labor Market Segment: All workers

c. Years: 2007, 2017

d. Job type: All jobs

e. [GO!] i. After results appear, use the left-side menu to make a Detailed Report, which you can

Export to Microsoft Excel ® format for later use.

Why not clip ‘em like we clipped the flowlines? Clipping your political polygons might lead you to the same correct answer, but the cost of doing so includes: 1) creating duplicate versions of authoritative data; 2) wasting disk space; and 3) cluttering your database with bits of data that have no practical use (other than answering this one question). Querying is simpler and more computationally efficient than performing spatial overlay analysis. Query whenever you can. Break or alter existing geometry only when needed.



Refresh your browser to reload the ONTHEMAP app and completely clear your previous search. OnTheMap

will let you use a shapefile to define a custom study area. So, you need to either copy or export your CCW polygon into shapefile format.

1. Use the Import Geography option to focus on the watershed.

2. Import from SHP a. When prompted, assign the correct parts of your CCW shapefile. b. [Import]

3. Jump through a few hoops

a. Select All Polygons b. Continue with Selected Features c. [Confirm Selection]

4. Find the pop-up balloon and follow the link to Perform Analysis on the Selection Area:

5. Customize your Analysis Settings:

a. Home/Work Area: Work

b. Analysis Type: Area profile with the Labor Market Segment: All workers

c. Years: 2007, 2017

d. Job type: All jobs

e. [GO!] i. After results appear, use the left-side menu to make a Detailed Report, which you can

Export to Microsoft Excel ® format for later use.

Remember:

The Work Area profile can be used to analyze the jobs in the watershed – (including those jobs held by people that live there or commute to there).

The Home Area profile can be used to analyze the workers that live in the watershed – (regardless if they work there or commute elsewhere)

Neither of these reports represent non-workers (e.g., retired people, school-age children, or incarcerated persons). If you need population data, then use American Factfinder.

You now have the data you need to answer Question #5. This question cannot be answered directly with the data, however, for the data must be interpreted by you and turned into information by you. To facilitate interpretation, try sorting and re-sorting your tables. Look for maximum and minimum values (#) and relative shares (%). Look for changes in the maximum and minimum values over time. Calculate percent change over time (Eq.1). For those that completed Economic Geography, use the Location Quotient technique to make even more effective comparisons between your state and local shares (Emsi, 2012).

%∆ =𝑣𝑎𝑙𝑢𝑒𝑡2−𝑣𝑎𝑙𝑢𝑒𝑡1

𝑣𝑎𝑙𝑢𝑒𝑡1× 100 Eq. 1

Where: 𝑣𝑎𝑙𝑢𝑒𝑡1 is the earlier value measured at time 1 and 𝑣𝑎𝑙𝑢𝑒𝑡2 is the later value observed at time 2. ###########



7. How much CCW area (sq.mi, sq.km, percent of total area) is covered by different land covers?

c. From a bird’s perspective, much of the watershed area is covered by trees or forest?

d. From a rabbit’s perspective, how much of the watershed area is covered by impervious surfaces?

Bolstad, 2016: p180-181 Figure 4 presents two workflows that will generate the same output – a table that contains the number of cells in each zone by unique raster value. Even though both will produce the same table, they are not equally efficient.

Figure 4. Both workflows [A,B] will generate the same output, but not at the same speed. ###### The small tangent …

Open your CCW polygon attribute table, then add a new long integer field called VRTXCNT. Save your table edits.

Next, back in table view, right-click your new VRTXCNT field to access the Calculate Geometry tool; use it to find the number of vertices that outline your watershed polygon.

7.a. How many vertices define your watershed polygon?

A B

Tabulate Area

Tabulate Area



Next, add your LANDCOVER raster as a layer, then inspect its raster properties.

7.b. Compare the number of polygon vertices in your watershed polygon (from 7.a.) against the number of cells in your landcover raster (#rows * #cols). What does the difference have to do the computational efficiencies between the workflows shown in Figure 4.A and Figure 4.B?

Ok, the small tangent is over. Follow the workflow shown in Figure 4B, then use the results to accomplish objectives 7c and 7d. ###########

Bolstad, 2016; p571-577

8. Which bedrock lithologies (LITH1) underlie the CCW (sq.mi, sq.km, percent of total area)?

a. Build a map figure that shows AND build a paragraph that describes:

i. the spatial distribution of bedrock, by lithology, in the CCW

AND

ii. the spatial distribution of the Conodoguinet Creek and its tributaries

9. Which bedrock lithologies (LITH1) underlie the CC and its tributaries (mi, km, percent of total length)?

In your methods section, document every tool and parameter setting that you used to obtain your answers to Questions 8a and 8b. In your summary section, use your answers to Question 8.a.ii to help you explain why your flowline bedrock percentages (Q9) are so different than your watershed bedrock percentages (Q8). You got this.



Table 2. Commonly used spatial overlay tools for vector data.

Tool Description

Clip This tool extracts Input features (and their attributes) that overlay Clip features, but it does not add clip feature attributes to the output attribute table. In other words, this tool works like a geometric cookie cutter. Shape attributes are auto-recalculated if the output is directed to a geodatabase (but not if directed to a shapefile). Non-shape attributes are carried to the output attribute table but not altered (e.g., split or summed) by changes to the geometry.

Identity This tool creates a new feature class by overlaying the Input Features (of any class) with the polygons of the Identity Features. The input features or the portions thereof that overlay identity features will get the attributes of the identity features. Input features that do not overlay identify features will get NULL attributes. Shape attributes are auto-recalculated when output is directed to a geodatabase (but not to a shapefile). All non-shape attributes are carried simply into the output attribute table but not altered (e.g., split or summed) by changes to the input geometry.

Input features: 2 features with attributes Overlay features: 1 feature with attributes Output: 4 features, 2 with joined attributes, 2 with input attributes and NULL values for the overlay attributes



Intersect (AND)

This tool computes a geometric intersection of Input features (point, lines or polygons). Only features or portions of features that overlap (and their attributes) among ALL the inputs will be written to the output feature class. Shape attributes are auto-recalculated if the output is directed to a geodatabase (but not if directed to a shapefile). Non-shape attributes are carried to the output attribute table but not altered (e.g., split or summed) by changes to the input geometries.

Input features: 2 features with attributes Overlay features: 1 feature with attributes Output: 2 intersecting features with joined attributes

Union (OR)

This tool computes the geometric union of Input features. All input features and all their attributes will be written to the output feature class. Shape attributes are auto-recalculated if the output is directed to a geodatabase (but not if directed to a shapefile). Non-shape attributes are carried to the output attribute table but not altered (e.g., split or summed) by changes to the input geometries. NULL values are written to attributes where features do not overlap.

Input features: 2 features with attributes Overlay features: 1 feature with attributes Output: 5 features, 2 with joined attributes and 3 with input attributes and NULL attributes from the other feature class



Table 3. Commonly use data reduction tools for vector data

Tool Description

Summary Statistics

This tool is used to reduce a large table (or a large set of selected records) to a smaller table by calculating summary statistics for a field or group of fields. Available summary statistics are:

SUM—Finds the total value for all records of the specified field.

MEAN—Calculates the average for all records of the specified field.

MIN—Finds the smallest value for all records of the specified field.

MAX—Finds the largest value for all records of the specified field.

RANGE—Finds the range of values (MAX minus MIN) for the specified field.

STD—Finds the standard deviation on values in the specified field.

COUNT—Finds the number of values included in the statistical calculations. NULL values are not included in counts. To determine the number of null values in a field, use the COUNT statistic on the field in question, and a second COUNT statistic on a different field that does not contain nulls (for example, the OID if present), then subtract the two values.

FIRST—Finds the first encountered value among records and reports it.

LAST—Finds the last encountered value among records and reports it.

Dissolve Use the Dissolve tool when you want to aggregate features that share a common attribute value or a common set of attribute values. Notice the groups of county polygons on the left that share a common attribute value (same color), then notice how the interior lines among those counties have been dissolved in the output. The only interior lines that remain in the output are those that separate features with different attribute values. Shape attributes are auto-recalculated if the output is directed to a geodatabase (but not if directed to a shapefile). Non-shape attributes that don’t define the dissolve are not carried to the output.

Input features: 97 features with attributes Output: either 6 single-part features with dissolve attributes OR 4 multi-part features with dissolve attributes



Table 4. Commonly use methods for reclassifying values

Tool Description

Field Calculator

The Field Calculator tool can be used to reclassify many values in an attribute table by adopting some IF-ELSE logic using Python. The logic is applied one record at a time. In short:

1. the user defines a new function in the code block; 2. the function is applied to one (or more) input fields; and 3. the new code is returned to the output field

By using this method, the user can avoid performing a lengthy series of select-by-attribute-then-calculate-field operations until all the desired values are reclassified.

Outputfield=

recodeMyStuff(!inputfield!)

Code Block

def recodeMyStuff(inval):

# put values in the comma separated list below

if(inval in [,,,]):

outval = 1

else:

outval = 0

return outval

Reclassify Use this tool to reclassify (or change) the values in a raster. Individual values or ranges can be reclassified so long as there are no overlaps. If the input raster has an attribute table, it will be used to create the initial reclassification table.

Input: 1 raster with 20 unique values or codes Output: 1 raster with new codes



Complete a well-written report of the lab exercise. Your report should be organized using five sections with headings: Purpose, Objectives, Methods and Data, Results and Answers, and Summary.

All lab reports should be typed and printed on 8.5” by 11” stock. Before drafting your report, set all page margins to be 0.7”, but set your left margin to 1.2”. Put your lab title, your name and the date in the document header. Set the normal font face to be Bookman Antiqua, Bookman Old Style, or Georgia; never use Times New Roman or any kind of decorative font. Also, set the normal font size to be 11 points. Major section headings should be in bold face and left justified. Use 1.5 line spacing. Include page numbers on every page. All tables and figures must be inserted into the body of your report and conform to the formatting and margin requirements. Build concise Purpose and Objectives sections using your own words. Your Methods and Data section should describe the sequence of GIS operations you used to answer each question, especially Questions 8 and 9. If it helps, sketch or make flowcharts to illustrate your workflows. If it helps, include screen shots of the tools you set up. Also, don’t forget to explain why workflow B is more efficient than workflow A (see Figure 2). Your Results and Answers section should contain your answers to the questions asked. Use the Summary section to do three things: 1) write a paragraph that tells a data-driven story about the watershed’s site and situation (see Elrod, 2014; Rodrigue, 2013); 2) use your describe anything that you learned (a light-bulb moment), found interesting or difficult; and 3) to address whether or not you accomplished the purpose of this exercise.

All students in the Department of Geography-Earth Science are expected to cite their sources of data and information using the citation style described by the Council of Science Editors (CSE). Students that are familiar with the MLA or APA citation styles will find, by comparison, broad similarities among the pieces of information that comprise a full citation, but some differences in how those pieces are ordered and presented. Full citations for data sources contain five parts (see the example below), which are separated by periods. Full citations are left-justified and use hanging indentation. Titles are highlighted using italicized text. The format:

Data Provider. Publication Year. Dataset name, vintage year if available. Data Distributor. “Last accessed online on” + date + “ at ” + URL

An example:

Pennsylvania Department of Transportation. 2019. Pennsylvania municipality boundaries. Pennsylvania Spatial Data Access. Last accessed online on February 19, 2019 at http://www.pasda.psu.edu/uci/DataSummary.aspx?dataset=41

http://www.pasda.psu.edu/uci/DataSummary.aspx?dataset=41



data provider: the person or party responsible for creating a dataset. Also known as the data originator, data creator, or author(s).

data distributor: the person or party responsible for hosting and distributing the dataset to others; may or may not be the same as the person or party that created the dataset.

Elrod, Zach. 2014. Site and Situation Review. YouTube. Last accessed online on February 19, 2019 at https://www.youtube.com/watch?v=f3on9hVWJkI

Emsi. 2012. What is Location Quotient? [Internet]. YouTube.com; [cited 10 Nov. 2017]. Available from https://youtu.be/Ri6HPshDnWQ

Pennsylvania Department of Conservation and Natural Resources. 2001. Statewide Geologic Map and GIS

Datasets. Pennsylvania Department of Conservation of Natural Resources. Last accessed on 20 February 2019 at https://www.dcnr.pa.gov/Geology/PublicationsAndData/Pages/default.aspx

Pennsylvania Department of Transportation. 2019. Pennsylvania county boundaries. Pennsylvania Spatial Data Access. Last accessed online on February 20, 2019 at http://www.pasda.psu.edu/uci/DataSummary.aspx?dataset=24

Pennsylvania Department of Transportation. 2019. Pennsylvania senatorial boundaries. Pennsylvania Spatial Data Access. Last accessed online on February 19, 2019 at http://www.pasda.psu.edu/uci/DataSummary.aspx?dataset=39

Pennsylvania Department of Transportation. 2019. Pennsylvania state house district boundaries. Pennsylvania Spatial Data Access. Last accessed online on February 19, 2019 at http://www.pasda.psu.edu/uci/DataSummary.aspx?dataset=53

Pennsylvania Fish and Boat Commission. 2017. Access Points (Fishing and Boating). Pennsylvania Spatial Data Access. Last accessed online on February 19, 2019 at http://www.pasda.psu.edu/uci/DataSummary.aspx?dataset=984

Pennsylvania Environmental Council. No date. Statewide Water Trail Program. Last accessed online on 19 Feb. 2019 at https://pecpa.org/program/statewide-water-trail-program/

Rodrigue, Jean Paul. 2013. The Geography of Transport Systems: Site and Situation. Last accessed online on December 1, 2015 at http://people.hofstra.edu/geotrans/eng/ch1en/conc1en/sitesituation.html

US Census Bureau. 2019. OnTheMap. US Census Bureau. Last accessed online on February 19, 2019 at http://onthemap.ces.census.gov/

US Geological Survey. 2019. NHDPlus High Resolution (HR). US Geological Survey. Last accessed online on February 19, 2019 at https://www.usgs.gov/core-science-systems/ngp/national-hydrography/nhdplus-high-resolution

University of Vermont Spatial Analysis Lab. 2016. High-Resolution Land Cover, Commonwealth of Pennsylvania,

Chesapeake Bay Watershed and Delaware River Basin, 2013. Pennsylvania Spatial Data Access. Last accessed online on February 20, 2019 at http://www.pasda.psu.edu/uci/DataSummary.aspx?dataset=3193

https://www.youtube.com/watch?v=f3on9hVWJkI

https://youtu.be/Ri6HPshDnWQ

https://www.dcnr.pa.gov/Geology/PublicationsAndData/Pages/default.aspx





https://pecpa.org/program/statewide-water-trail-program/

http://people.hofstra.edu/geotrans/eng/ch1en/conc1en/sitesituation.html



Documents

Spatial analysis and sptial models....Spatial data sources, data reduction methods & spatial analysis Read everything before doing anything! 2 1. Describe in words where is the CCW