Practical 5 Onshore Wind Turbines Site Suitability- Data Sourcing Notes

Introduction

• Gathering and formatting spatial data is a significant part of any GIS project.
• This session focuses on sourcing and formatting data for a new windfarm development in the District of Lancaster.
• The compiled datasets will be used in the following session to identify suitable sites for a new windfarm.
• Ensure the final versions of each dataset are stored safely for next week’s session.
• Onshore windfarm planning applications are determined by local councils in the UK.
• There are no strict rules for site suitability, but physical constraints impact feasibility.
• Criteria will be supplied, with suggestions for additional options to incorporate in the analysis.
• Complete as much as possible during the session, with any remaining tasks to be done before the next session.
• Next week will involve simple and weighted map overlay analysis using raster datasets, utilizing today's inputs.

Best Formats for Download

• There are many data formats that can potentially be converted and used in a GIS, however, there are a small number of common formats you are most likely to come across.
• The most common vector and raster formats used in ArcGIS Pro are as follows:
  • Vector (feature) data
    • Geodatabase (.gdb) – although this is rarely an available option
    • Shapefile (shape, .shp) – this is a commonly available download format that is easy to add straight in to ArcGIS
  • Raster data
    • Geotiff – these can be added directly into ArcGIS and are suitable for all kinds of raster data
    • .asc – for gridded data – easy to work with or convert in ArcGIS
    • .img – particularly for remote sensing data

Data delivery: Zip files

• Almost all data are downloaded in compressed zip files.
• The contents of these files need to be extracted before being added to ArcGIS.
• To extract the files right click on the file in File Explorer and select Extract All, specifying a suitable location for your output files.

Where to store data

• Store feature (vector) data within a file geodatabase.
• You will only need one geodatabase for your own project work and can organize data within this into feature datasets (think of these as intelligent subfolders).
• Raster datasets can be stored within a geodatabase or within a folder.
• If you save a raster dataset within a geodatabase it must not have a filename extension.
• If you save a raster dataset outside of a geodatabase (in a normal folder) it must have a filename extension, e.g., landcover.tif.

Summary of this session

• In this session we will explore a variety of data sources that are widely available for Great Britain.
• Throughout this session we will also record metadata relating to the datasets that we download.
• You will need to complete and submit an equivalent metadata form with your project report early next term.
• By the end of the session, you will have all your data prepared for next week’s session.

Learning Objectives:

• Section 1 aims to introduce you to a variety of different data sources and how to download/access them and add to an ArcGIS Pro map view
• Section 2 shows you how to generate raster layers relevant to our windfarm study from different inputs (feature and raster datasets)
• Section 3 gives you the opportunity to source and prepare your own additional data should you wish to do so
• Section 4 explores how to manage ArcGIS projects in terms of file management and metadata

Learning Outcomes:

• By the end of this session, you will to be able to:

Section 1:

• Acquire data from a website or data provider, collate the associated metadata and import the data into ArcGIS Pro
• Change the appropriate settings to alter the processing extent of an ArcGIS project
• Use the Mosaic to New Raster tool to combine raster tiles into one raster

Section 2:

• Use the Reclassify tool to create a simplified raster dataset from a more complex input raster dataset
• Create a continuous raster from point (vector) data using the IDW tool
• Change the format of vector data to raster data using the Polygon to Raster tool
• Use the Line Density tool to create a raster from a polyline road feature that shows the density of roads per $km^2$
• Use the Euclidean Distance and Cell Statistics tools to create rasters from vector data that define thresholds for “within a distance” criteria
• Use the Slope to tool generate a slope raster dataset from a digital elevation daatset

Section 4:

• Manage your storage on your H:/ drive by removing data that is no longer needed
• Create, Add and Export appropriate Metadata from ArcGIS Pro

Getting started

1. Launch ArcGIS Pro with a new Map.
2. Save this within your H:\LEC314 folder, naming it Windfarm [make sure that the option to create a new folder is selected and that there is no space in the name Windfarm as spaces in folder names cause problems for some raster analysis]

1. Data Sourcing

• In this section, we will download and prepare data for Lancaster District which we will use next week to map suitable areas for a new windfarm.

1.1 Boundary data

• The first dataset that we will source is a district boundary for Lancaster. For most administrative or statistical boundaries in the UK one of the most useful data services to use is the UK Data Service
• Open https://borders.ukdataservice.ac.uk/ in a web browser
• Click on Boundary Data Selector
• Under the Select options, change the Country to England, Geography to Administrative and Dates to 2011 and later
• Click Find
• In the list of Boundaries select English Districts, UAs and London Boroughs, 2011
• Click List Areas
• Select Lancaster from the list
• Click Extract Boundary Data

Once the file is ready to download:

• Click on BoundaryData.zip to download the file
• Extract the data to your Windfarm folder

We can now add the data to the map.

• In ArcGIS Pro, add englandlad2011.shp to your map (Hint: Use Add Data)
• Change the symbology of englandlad2011 so that it has a transparent fill and a clearly visible outline.

1.2 Basemap layers

• There are several basemap layers available to use in ArcGIS. You are free to experiment with different options available. Before continuing we recommend that you:
• Change the Basemap to GB Cartographic

Q1: On the coastal side of the district, does the boundary follow the low or high water mark?

1.3 Metadata

• When working on a project it is important to keep track of metadata, which is information about the dataset you have downloaded. This is critical for referencing purposes. In some cases metadata files can copied or imported to an item, but for this case we will have to add this manually.
• Firstly, it is useful to look at the supporting files that were supplied with the data.
• In File Explorer go to the folder your Windfarm folder
• Notice that there is both a README.txt file and TermsAndConditions.html
• Open both of these files to find out what each contains.
• On this occasion there is little useful information in the README file, however, the TermsAndConditions file contains some helpful information.
• We can see that the data was created by the Office for National Statistics and supplied via the UK Data Service Census Support. We can also see the year the data relates to and the copyright information. We can now edit the metadata in ArcGIS Pro. Note on this occasion that no information is provided regarding the scale or resolution of the dataset.
• Return to ArcGIS
• Right click on englandlad2011 in the Contents pane and select Properties
• Click on Metadata and in the drop-down menu select Layer has its own metadata
• Fill out the rest of the Metadata using the TermsAndConditions file.
• Click OK

1.4 Spatial Extent

• Before we continue, we will set our analysis extent to the boundary of Lancaster District. This will restrict spatial extent of the output of any geoprocessing steps we undertake to the extent of the district.
• Change the Processing Extent (Hint: Analysis tab-> Environments) and select englandlad2011 [this limits processing to the rectangular extent of the district
• Make a note of the coordinates (round to the nearest metre) now listed in the processing extent, as these will prove useful for later data download steps.

We will also set the raster analysis mask to the district boundary. This will crop the raster outputs we create later to the shape of the boundary.

• Under Mask select englandlad2011
• Click OK

1.5 Existing onshore wind turbines

• Before we begin importing further data, we will explore using datasets available on ArcGIS online. We will explore ‘active’ and ‘refused’ onshore wind turbine developments.
• Click on Add data, go to the ArcGIS Online portal and search ‘onshore wind uk’
• Add the layers ‘Active UK Onshore Wind Turbines’ and ‘Refused Onshore Wind Turbines’ created 06/08/2021 then zoom to the extent of these layers. Explore these layers across the country and then across the District of Lancaster to understand where planning permission has been granted and refused. You might want to change the point symbology to differentiate more clearly between ‘active’ and ‘refused’ onshore turbines. (If the points do not display on your screen at higher zoom levels simply open the layer properties, click on the General tab and set the In beyond (maximum scale) to ).
• Once you have finished, turn off the display of these layers.

1.6 Land cover

• Given that the boundary of the district extends into the sea, we will use 25m land cover data created by UKCEH to define land areas suitable to build on. These data are available to download via Edina Digimap Environment, however, as this service only allows us to download for the whole country, we will add in a pre-existing copy stored on a local server. We first need to establish a connection to the database where this is stored.
• Add a folder connection to \lancs\luna\fst\lec\gis\gisdata\national (Hint: Right click on Folders in Catalog window-> Add Folder Connection)
• Add the Raster Dataset lcm2021 to the map. [Hint: Add Data, National folder, lcm2021].
• This dataset covers England and Wales. Change the default symbology by choosing Symbology then choosing the Import from Layer File option choosing the layer file LCM2021.lyrx from the same folder on the server.
• We do not need to do anything else to this raster dataset at this stage, as the data will be cropped to the extent of the study area when we reclassify suitable land areas later in the session.
• Repeat steps 19-22 above to fill out the Metadata using the information of the UKCEH Environmental Information Data Centre webpage Land Cover Map 2021 (25m rasterised land parcels, GB) - EIDC (ceh.ac.uk)

1.7 Wind

• The wind data that we will be working with was downloaded from the DTI Wind speed database https://webarchive.nationalarchives.gov.uk/20070604025700/http://www.dti.gov.uk/energy/sources/renewables/renewables-explained/wind-energy/page27708.html
• These data are supplied as tables containing British National Grid coordinates with associated average annual wind speed values in metres per second. The points are spaced 1km apart.
• Later in the session we will return to this dataset to interpolate the point data into a continuous (raster) surface representing wind speed.
• If working elsewhere around the world, global climate data, including wind speed, is available at up to 1km resolution from http://worldclim.org.
• Today we will add in the wind data from a copy of the data archive stored on a local server.
• Turn off the display of LCM2021
• Use Add Data to add windspeed45.txt from the DTIWindPSeedDatabase subfolder within the National folder

This file contains the wind speed values 45m above ground. In the same folder as the data you have added there is also a README file which contains more information about the dataset.

• Display XY Data for the windspeed45.txt file, naming the Output Feature Class windGB [Hint: (X) Easting, (Y) Northing, BritishNationalGrid]
• Zoom to the extent of this layer
• This dataset currently covers the whole of Britain. We will now clip this data to just our study area making the dataset more manageable to work with.
• Clip the windGB to englandlad2011 naming the Output Feature Class as windpoints
• Remove windGB from the Contents pane [right click on the layer and select Remove]
• Zoom to the extent of the windpoints layer
• Change the Symbology for windpoints to better show variation in wind speed across the study area. [Hint: Graduated Symbols, field as WSpeed]

Q2: What is the average annual wind speed around Lancaster University? [Hint: Select the 4 surrounding points]

• Update the Metadata as you have before with the information from the README file
• Turn off the display of windpoints before continuing

1.8 Designations (SSSI)

• We will now download boundaries for Sites of Special Scientific Interest (SSSI). This dataset is produced by Natural England and can be accessed over the cloud (ArcGIS Online portal) or downloaded from data.gov.uk as an opensource dataset.
• In this case, we will download the dataset.
• Data.gov.uk provides access to many data sets produced by a variety of local and national government organizations. There is a lot of useful data available here, although sometimes it takes a little effort to find what you need.
• Open https://data.gov.uk/ in a web browser
• Search for Sites of Special Scientific Interest
• Click on the link for Sites of Special Scientific Interest (England) last updated 30 September 2024.

Note it is not always obvious which of the data links (if any) will contain the data you need. WMS stands for Web Map Service, which rarely gives you access to download the files. WFS is a web feature service. In this case the most obvious link to try is the one labelled Download.

• Click on the link SitesOfSpecialScientificInterestEngland_Download
• Under Select your area toggle the option to Area of Interest
• Click on the Polygon tool and draw an area that covers Lancaster District (it is OK to extend slightly beyond the extent you need). Double click to stop drawing the polygon. [You may need to visually cross reference with the boundary of the district shown in ArcGIS].
• Under Download your Data click on the Shapefile option
• Once the data is ready click on the Download link
• Extract the data from the zip file, saving it within your Windfarm folder
• Return to ArcGIS Pro

We are now ready to add the SSSI data to the map

• Use Add Data to add the SitesofSpecialScientificInterest_EnglandPolygons.shp from your Windfarm folder to the map [if this is not visible try clicking refresh the view and navigating into the data folder]
• Zoom to the extent of the SSSI layer

Q3: How many SSSIs intersect with Lancaster District? [Hint: use select by location]

If there are less than 41 features in your SSSI layer, you need to download the data again making sure that you select a bigger search area

• Update the Metadata as before with information about this dataset (information can be found in the docs folder which is produced when you extract the data)
• Clear selection and turn off the display of SitesofSpecialScientificInterest_England Polygons before continuing

1.9 Digimap Download

• The next two datasets that we will explore are all available to download from Edina Digimap.
• We will download these together then explore them individually.
• In a web browser go to https://digimap.edina.ac.uk/ then Log in
• Click on Ordnance Survey -> OS Digimap -> Download Data -> Use Coordinates
• Type in coordinates for the extent of Lancaster District based on those that you noted earlier in the Spatial Extent section [Take time to carefully work out which boxes the coordinates go in, as they way the information is shown in ArcGIS and entered in Digimap is slightly different]
• Click GO [the window should now zoom in to a rectangular extent around Lancaster District – if not, check your coordinate extent]
• Expand the options for Land and Height Data and select OS Terrain 50 DTM
• Expand the options for Vector Data and select OS Open Map Local
• Click Add to Basket
• Select the format for Terrain DTM to be ASC and the format for OS Open Map Local to SHAPE
• Click Request Download
• Login to your email ready to receive emails from Digimap confirming your data order, then providing the link for your data download.

If there is a slight delay in receiving the email confirming that your data is ready to download, you can complete the steps to prepare the rivers data (Section 1.10) then return to download the data.

• Check your emails for an email from Digimap telling you your data is ready to download [if this is not yet ready, there is a backup copy of the download file available on Moodle which you can use]
• Follow the Download your data link in the email
• Click Download
• Open the downloaded file and extract all files to your Windfarm folder [Be patient – this is a big file!]

1.10 Rivers

• We will now extract rivers for the study area from the Ordnance Survey Open Rivers database.
• These data are available to download from Edina Digimap, however, as this only allows you to download the dataset for the whole country, we will extract the data we require from a copy of the dataset already held locally on the university server.
• Open the tool Clip (Analysis Tools)
• For Input Features click Browse then under Folder select National > National.gdb and navigate into OSOpenRivers. Select OpenRivers and click OK
• Set the Clip Features to be englandlad2011
• Name the Output Feature Class Rivers
• Click Run

Rivers within the district boundary should now appear on the map.

Q4: Examining the pattern of the rivers with the district boundary, what natural feature does the administrative boundary follow in many places?

Further information on this dataset that will help you complete the metadata form is available on the Digimap web page.

• Return to Digimap
• Follow the link https://digimap.edina.ac.uk/webhelp/os/osdigimaphelp.htm#datainformation/osproducts/osopenrivers.htm to find out more about OS Open Rivers
• Update the Metadata as before with information about this dataset

1.11 Woodland

• We will add in areas of woodland from the OS OpenMap Local data we downloaded from Digimap
• Use Add Data to add SD_Woodland.shp to the map [Hint: Navigate to your Windfarm folder, click refresh, then navigate to the folder open-map-local]
• We are now going to explore how well represented woodlands are between the OS OpenMap Local and LCM data.
• Turn off all layers except SD_Woodland
• Change the Basemap to Imagery
• Change the symbology of SD_Woodland so that it has no fill and a clearly visible outline, e.g., yellow. This will aid our interpretation relative to the aerial photography.
• We will now explore some areas of the map in more detail
• Use the Locate tool to find the coordinates 351780, 466490
• Change the scale box (at the bottom left of the map) to read 1:5,000
• Consider the accuracy of the woodland outline for this area, then turn on the display of lcm2021

Q5: Which layer (SD_Woodland or lcm2021) provides the best representation of woodland in this area?

• Explore the map further being critical of how accurately the woodlands on the aerial photography are represented by the vector and raster products
• For our subsequent analysis we will use the OS OpenMap Local representation of woodland, however we will remove some of the smallest areas of woodland that represent only a few trees.
• Open the attribute table for SD_Woodland

Notice at present that there is no attribute for the area associated with the features. This is partly down to the current data format – a shapefile. We can overcome this by exporting the data we require into a geodatabase.

Data formats: Shapefiles v Geodatabase

• Both of these data formats can be created, edited and analyzed within ArcGIS.
• Shapefiles are a ‘simpler’ data format that lack topology and do not have dynamically updated attributes such as length and area.
• Shapefiles, do however have the advantage of being able to be read into many different GIS software packages and are a commonly available format when downloading data.
• For the majority of this course we work with feature classes stored within a geodatabase. This forces data into more intelligent (and dynamic) forms.
• The geodatabase is ESRIs preferred data format for feature data and has advantages, particularly with regards to automatic updating of length and area attributes, however, this format is less easily read into other software packages.
• We will now clip the data saving it in our geodatabase.
• Use the Clip tool to clip SDWoodland to the district boundary (englandlad_2011) naming Output Feature Class Woodland
• Turn off the display of SD_Woodland
• Zoom to the extent of Woodland

Open the attribute table for Woodland

Notice that there are now attributes for ShapeLength and ShapeArea.

Shape_Area refers to the area of the feature in map units, which for the British National Grid means $m^2$.

Q6 What is the smallest area of woodland?

• We will now remove all areas of woodland less than 1000$m^2$.
• Make sure no features are currently selected then use Select by Attribute to select all woodland features with an area less than 1000.
• At the top of the attribute table click Delete
• All the selected features will now have been removed from the dataset.
• On the Edit ribbon click Save
• Click Yes to confirm you wish to save all edits

Note: that this processes permanently deletes features from the feature class. For a less permanent solution we could have used a definition query to create a temporary subset of the data.

• Before continuing, remove SD_Woodland from the map as we no longer need this.
• Update the Metadata as before with information about this dataset [For information about any data sets downloaded from Digimap return to the Digimap Download window and click on > next to the dataset in the list to view information such as scale, date etc]

1.12 Water Bodies

• We will now add in water bodies from the OS OpenMap Local data we downloaded earlier.
• Add SDSurfaceWaterArea.shp to the map

Notice that the data has downloaded as a 100km tile and not just for the area that we require. In this case SD in the file name relates to the 100km British National Grid tile that the data is associated with.

• As with woodland we will remove the smallest water features, typically small ponds, from our analysis. In this case to avoid removing short sections of river (represented as polygons) we will select features to delete based on the length of their perimeter rather than their area.

Q7 Why is perimeter length rather than area useful in this example?

• Clip SDSurfaceWater to the district boundary (englandlad_2011) naming Output Feature Class SurfaceWater
• Turn off the display of SDSurfaceWaterArea
• Zoom to the extent of SurfaceWater
• Repeat steps 99-102 on your new layer to remove surface water features with a Shape_Length less than 300
• Update the Metadata as before with information about this dataset

1.13 Roads

• We will now add in roads from the OS OpenMap Local data that we downloaded earlier.
• Add SD_Road.shp to the map and clip it to the district boundary (naming it Roads) and then remove the original .shp file from the map.

Note: For the purposes of this exercise we are using OS OpenMap Local data. This provides a similar spatial representation to the OS Open Roads data that we used in week 3, however, has less attributes. If you only require road data, or require additional attributes it would be better to work with the OS Open Roads data.

• For this study, the OS OpenMap Local data is sufficient and we have already downloaded it. We do, however, require an attribute for road length, so as in the previous example, we will clip this dataset and save the output in our geodatabase (generating Shape_Length in the process).
• Update the Metadata as before with information about this dataset

1.14 Buildings\Settlements

• We will now add in buildings from the OS OpenMap Local data we downloaded earlier.
• Add SD_Building.shp to the map

We do not need to do anything else to this layer at present. It is only layers that need additional attributes, such as length or area, that need to be transferred into the geodatabase.

• Update the Metadata as before with information about this dataset

1.15 Elevation

• The elevation data we have downloaded has been delivered as 25 separate raster tiles. To make this easier to work with we will begin by converting this into a single raster layer using the Mosaic to New Raster tool.
• Search for and open the Mosaic to New Raster tool
• Click on the browse button next to Input Raster then navigate to …\Windfarm\terrain-50-dtm\sd then change the type (bottom right corner of Add Data window) from All Supported Types to Rasters (All Local Types).
• Select all of the raster files then click OK
• Set the Output Location to be Windfarm.gdb
• Name the Raster Dataset elev50
• Change the Pixel Type to 16 bit signed [click on next to pixel type for more explanation of what the data ranges different pixel types are able to represent]
• Enter the Number of Bands as 1
• Click Run
• Update the Metadata as before with information about this dataset

Q8. What is the elevation range (m) across the mosaicked dataset?

There are many other elevation data sources you could choose to work with, these include:

• LiDAR data. 25cm – 2m spatial resolution. High vertical accuracy. Available as terrain (DTM) and Surface (DSM) models. Available for multiple years for most of England via https://environment.data.gov.uk/
• OS 5m DTM (via Digimap)
• ALOS 30m – Global data https://www.eorc.jaxa.jp/ALOS/en/aw3d30/index.htm
• Save your Project before continuing

2. Preparing Primary Raster layers

Wind Farm Criteria

• Next week we will use map overlay to determine locations of suitable sites for a new wind farm. Currently in the UK, onshore windfarm planning applications are determined by local councils, consequently there are no hard and fast rules for what determines site suitability, although there are physical constraints which will make a windfarm feasible or not.
• In the final section of this session, we will prepare primary raster layers ready for the analysis, making use of the datasets we have already downloaded and prepared.
• At the end of this session we are going to give you the opportunity to consider additional criteria that you may feel appropriate for your analysis. As we said earlier, there are no hard and fast rules!

Criteria:

1. Should be within Lancaster district
2. Should not be built in coastal areas or bog
3. Annual average wind speed should be greater than 6 m/sec-1
4. Sites should not fall within a designated SSSI
5. Sites should be in areas with less than 2km of road per $km^2$
6. Sites should not fall within 200m of existing water
7. Sites should not fall within 200m of existing woodland
8. Sites should not fall within 100m of a building
9. Sites should be situated on a slope less than 5 degrees

Before we create our primary rasters, we will set the spatial resolution for our analysis. For this exercise we recommend a resolution (cell size) of 50m as this captures reasonable detail for our study area without creating excessively large files. The cell size chosen should always reflect your data inputs, level of detail required and size of study area. It should not always be 50m!

• We will now specify the cell size in the Environment Settings.
• Change the Cell Size type (Hint: Analysis tab-> Environments) to 50

2.1 Mask: within the Lancaster District

• The analysis mask is currently set to the boundary of Lancaster District. This fulfills the first criteria ‘should be within Lancaster District’ and there is no need for us to convert this layer to raster.

2.2 Mask: Suitable land within Lancaster District

• We will now refine this further by creating a mask of suitable land cover types, and therefore fulfilling criteria 2 that wind farms ‘should not be built in coastal areas or bog’. We will achieve this by reclassifying the CEH land cover data that we added earlier using the Reclassify tool.
• Before we compute the reclass we first need to work out what values are associated with the land cover classes that we wish to exclude.
• Open the Symbology pane for lcm2021
• You should now see a value listed next to a label that contains a description of the land cover type e.g. 1, Broadleaved woodland.
• Make a note of the values associated with the following land cover types that we do not wish to build on:
  • Land cover type Value
  • Fen, Marsh and Swamp
  • Bog
  • Saltwater
  • Supra-littoral rock
  • Supra-littoral sediment
  • Littoral rock
  • Littoral sediment
  • Saltmarsh
• Use the Reclassify (Spatial Analyst) tool to reclassify the values you listed above to NODATA [take care to ignore the existing values listed in the New column as these can be misleading] and the other remaining values (except NODATA) to a new value of 1 for lcm2021, naming the Output Raster Landmask.
• Zoom to the extent of this layer.
• Before continuing, we will set this layer as the analysis mask from now on.
• Change the Mask in the Environment Settings to Landmask (Hint: Analysis tab-> Environments)

2.3 Interpolation: Wind speed

• Turn on the display of windpoints
• These data currently only exists in point form. We therefore need to use an interpolation routine to convert values associated with individual point locations into a continuous raster surface.

Note, there are a number of different interpolation tools available in GIS. In this example we are going to use the Inverse Distance Weighting (IDW) method and adopt the default options provided.

• If you want to find out more about different methods for creating a raster from point features see here.
• Use the IDW (Spatial Analyst Tools) to convert the point feature windpoints to an output raster called Wind. [Hint: Z value as WSpeed]

ArcGIS creates a continuous surface representing wind speed for the study area extent at a spatial resolution of 50 metres.

2.4 Convert to Raster: SSSI

• As SSSIs are designated areas with distinct boundaries, to represent them as raster we simply need to carry out a polygon to raster conversion using the Polygon to Raster tool. When converting features in this way we have the option to retain one of the attributes from the polygon features. In this case we will retain the name of the SSSI.
• Use the Polygon to Raster tool to convert SitesofSpecialScientificInterestEnglandPolygon to an output raster dataset called SSSI, using sssiname in the Value field.

2.5 Line Density: Roads

• The Line Density tool calculates the density of linear features (e.g. roads) within the search radius of the raster/grid cell. The values generated are ‘units of length per unit of area’. For more information on how line density works see here
• Use the Line Density tool for the polyline features Roads to calculate road density (in square kilometers) within a search radius of 500m, naming the output raster RoadDense

2.6 Euclidean Distance

• Some of our criteria depend on thresholds within a distance of certain features, e.g. Sites should not fall within 200m of existing water. Before we can define those thresholds, we first need to create surfaces representing the distance from each of these features. In these examples we will calculate the Euclidean (straight line) distance from rivers, surface water, woodland and buildings.
• Use the Euclidean Distance tool for Rivers naming the output raster as DistRivers [Hint: leave the settings as they appear]

Now repeat the previous step (125) using the following input feature layers to generate the following output distance raster layers.

Input feature layer Output distance raster

• SurfaceWater DistSurfaceWater
• woodland DistWood
• SD_Building DistBuilding
• Ideally, we would like a single input raster for distance from water, combining distance from surface water areas (e.g. lakes and ponds) with distance from rivers, since this will form the input for one criteria in our later analysis. To combine these we will use the Cell Statistics tool to create a new raster that represents the minimum cell value from each of the 2 input rasters.
• Use the tool Cell Statistics (Spatial Analyst Tools) to preserve the minimum values for the both the DistRivers and DistSurfaceWater rasters, naming the output raster DistWater [Hint: Overlay statistic to Minimum]

Q9. Why in this example is it appropriate to preserve the minimum value of these two rasters and not the mean or maximum? [Hint: think about the criteria for distance from water]

• We no longer need the two separate raster that we have just combined. We will therefore remove them from the Contents pane.
• Remove DistRivers and DistSurfaceWater [Hint: Right click on each in the Contents pane and select Remove]

2.7 Slope

• The final raster input that we need to create today is Slope. Slope is derived from a digital elevation model using the Slope tool. It is also possible to easily compute other surface outputs such as aspect or hill