EcoServ-GIS v3.3 Technical Report: “BaseMap (Habitat Map)”...information on habitat type / land cover. Land cover and habitat mapping data can be used to add additional information

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EcoServ-GIS v3.3

Technical Report: “BaseMap (Habitat Map)”

To be able to model and map ecosystem services, information is required on land‐use and habitat type

across the study area, together with socioeconomic data on the location and characteristics of the human

population. No single dataset describes the landscape in enough detail and at a fine enough resolution for a

service‐based modelling approach, at a county scale. Therefore EcoServ‐GIS uses a range of available

datasets to update the attributes of a fine scale vector dataset, OS MasterMap. The models within the

BaseMap Toolbox process various datasets, including several optional datasets, to create a habitat

“BaseMap” of the study area.

The main input dataset for these models is the Ordnance Survey MasterMap vector data. This high

resolution dataset is the most comprehensive national mapping data available and is available to local

authorities under the Public sector Mapping Agreement (PSMA) or One Scotland Mapping Agreement

(OSMA). All potential data sources that were considered as source data for EcoServ‐GIS have their

limitations. Potential habitat and land cover data sources that were considered include; European Corine

Biotypes, CEH Land Cover Map 2007, County Phase 1 habitat maps (paper, scanned or digitised), Biodiversity

Action Plan (BAP) inventories (national and local), Landscape Character mapping (Landscape Description

Units). In selecting source data for EcoServ‐GIS the following considerations were made; cost, availability,

licensing issues, mapping accuracy, transferability to different study areas, minimum mapping unit

/resolution, data age and update frequency. Considering the importance of human ‐ environment links and

the requirement to address a number of urban / urban‐fringe ecosystem services the OS MasterMap was

selected as a key source dataset on which to base the ecosystem / habitat mapping. This builds on the work

of the Mersey Forest utilising this data for Green Infrastructure mapping (The Mersey Forest, Butlin,

Chambers, & Ellis, 2011). OS MasterMap allows classification of a wide range of ecosystems / habitats and

contains a wide range of accurately mapped features of use in service mapping.

The models and Toolkit have been developed so that they can be run initially using only the OS MasterMap

data as the sole data input (without any optional data). This may be useful as an initial test of the BaseMap

and selected Ecosystem Service maps for a new Study Area. However using OS MasterMap as the only data

to map habitat / ecosystem location is not recommend as this data only holds a selected range of

information on habitat type / land cover.

Land cover and habitat mapping data can be used to add additional information to characterise the polygons

present within OS MasterMap. Depending on the country and study area and resources available to a project

a range of data can be accessed and used. Following many years of Biodiversity Action Plan projects data

may be available on the location of important semi‐natural habitats at a county, or country scale. For urban

and urban fringe areas local authority Open Space Survey / Greenspace / Green Infrastructure mapping can

be used to classify areas. Remote sensing information from the European Corine Land Cover data or CEH

Land Cover Map 2007 can be used to characterise rural habitats. If some of these data are not available then

locally produced Landscape Character mapping / Landscape Descriptive Units may be used to classify areas

by dominant agricultural type. Such Landscape Character mapping may however be very broad or may

require interpretation and editing to allow its use in a GIS.

The result of the combined use of several input data sources to add available habitat / ecosystem data is a

system whereby the “BaseMap” can be considered to represent the "best‐available" ecosystem map. The

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models developed from this map will be more accurate where the date of source data is more recent, the

surveys were more comprehensive and accurate. These limitations must be acknowledged when using the

resulting maps. The maps can however be re‐run as further ecosystem / habitat data becomes available.

Users should check and be familiar with the content, scope and quality of the habitat BaseMap produced for

their study area, as all other subsequent analysis is based on this map.

Ideally a range of source data will be used to help build the Habitat BaseMap. The pros and cons of these

optional data are discussed below. The Toolkit User must become familiar with the relative impacts of

including or excluding potential source data. In most situations a suitable range of input data would be the

use of Open Space Surveys (GI), accompanied with LCM 2007 data. This risks losing information on known

occurrence of semi‐natural / BAP habitats at a local level but such information is often patchy and if the local

accuracy impacts of this are recognised the resulting maps should hopefully still be useful.

Ecosystem/habitatdata

Pros Cons

Open SpaceSurvey/GreenInfrastructure/Greenspace

Identifies the landuse/habitat typeofareasorGreenspace/OpenSpace,thereforegivesdetailedclassificationofurbanareas.

Data likely to be more comprehensive in urbanareasthanaroundvillagesandsmallertowns.

LandscapeCharacterAssessments(LCA)

Potentially complete coverage at acounty scale. Summarises the typicallandscape character and land useacrossmapped landscapeareas.Maybe useful for mapping agriculturalareas.

Variable between different counties. Theminimum size ofmapped areasmay not be verysmall. Landscape Description Units (LDU) datamay not be available. The categories ordescriptionsusedmaynotbesufficienttoallowanecosystem / habitat / land use type to beidentified.

BAPinventory(National)

Mapping of important semi‐naturalhabitats

Somedatasourcesverydated.Landuse/habitatmayhavesincechanged.Oftenveryvariabledatasources between habitat types.Metadatamay beverycomplex.

BAPinventory(Local)(LBAP)

Potentially more recent surveys anddata than national BAP inventory.Includes locally important habitattypes. May include smaller habitatpatchesandsitesoflowerqualitynotincluded in national inventories.Oftensurveyshavenotbeendigitisedor compiled. Coverage likely to beverypatchy.

Coverage of particular habitats is patchy.Generally assume that likelihood of accurateclassification and identification of patchesdecreases with patch size (larger patches aremorelikelytohavebeensurveyed).

Slopes(fromDTM)

Use of this data allow areas ofprobable unimproved habitat to beidentified based on likelihood ofimprovement / agriculturalintensification.

Mostusefulwhere5mor10mDTMareaavailable.Using slopes to reclassify habitat type is apredicted / modelled relationship and may notreflectrealityineverypolygon.

WoodlandSurvey(Scotland)

Allows the differentiation of semi‐naturalfromplantationwoodlands.

NotavailableinEnglandandWales.

LCM2007 Full coverage of UK, known errorrates.

Doesnotincludedetailedhabitatsinurbanareas.Cost/ license issues.Minimummappingunitsdonotincludesmallpolygons.

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GIS Analysis steps

Model:ES_01a_StudyAreaWithSeaSub‐Models:

S_11_Add_BAP_Fields_V1.

S_12_Add_LCM_Fields_V1.

S_13_Add_DataSources_Fields_V1.

S_z_All_Deletes_B1AX (S_z_buffer_SA_removed, S_z_StudyArea_removed, S_z_Base01_removed,

S_z_SA10_Sa50_removed, S_z_B1AX_removed, S_z_RASTER_B1AX_removed).

Main models:

Delete In Memory.

Calculate Value checks the status of the tick box “Use_Memory” and sets the workspace as either “In_Memory” or %Scratch%.

Sub‐models run to delete any data that may be present due to the model having previously run (old versions of the outputs).

o From “Scratch” any Feature Classes or Rasters containing *B1AX* are deleted. o From “Outputs” any Feature Classes with *buffer*, *study*, or *BaseMap01, or any Rasters with *SA* are

deleted.

Model takes the “MasterMap” data, Converts to feature layer, Selects only polygons > 0.7m.

Add Fields “Area_m” and “Length_m” and calculate these from “Shape_area” and “Shape_length” (this is so that these fields are available for use when the data is used In_Memory, because the Shape fields are not present when the data is In_Memory) .

Add and calculate field “Slvr_shp” . Calculate by (3.1415926535897932384626433832795 * (([Shape_Length] / (2 * 3.1415926535897932384626433832795)) ^ 2)) / [Shape_Area] . This is a shape index and allows the identification of small and narrow “sliver” polygons. Removes from selection polygons where Shape_area < 20 AND Slvr_shp > 15 AND NOT “DescGroup” = ‘Path’

Copy to Scratch.

Analysis conducted to create a grid that covers the extent of the Study Area plus buffer.

This is so a polygon is created that comprises sub parts and is not one whole polygon of the StudyArea. This is because when carrying out select operations, these are substantially quicker when using a multiple polygon StudyArea file than a single polygon of the whole extent.

StudyArea1 – make feature layer.

Add field “cvt”, calculate to “1”. Convert to raster at 5,000m cells (based on cvt), raster to points, point to raster (based on value ID of each point), raster to polygon. Union the Studyarea1 with the grid polygons, repair geometry

Analysis then conducted to create a buffer around the coast to create an area of Sea.

Analysis adds the Sea polygon to the MasterMap data, with appropriate data added to the MasterMap attribute fields.

Outputs created are: o StudyArea, SA_buffer, SA_buffer_grid, SA010, SA050, SA100.

Sub‐models add a range of data fields to MasterMap, as used by later analysis models (See data fields table)

Field DataOS Calculated with “OS” to label the resulting dataset as based on OS data source.

The model saves two main results / output files.

One file has a standard name and will always be named exactly as below. This is the file that is automatically searched for and used by later models.

A second, identical copy is saved with the day's date and an optional user generated "RunCode" as part of the file name. This file is for reference only.

o copied to %Outputs% / BaseMap01. o copied to %Outputs% / BaseMap01a_%RunCode%_%outDate%.

The “RunCode” is set to allow each run of the Models to be coded by a StudyArea and / or run specific code

Delete Tool run ‐ In Memory.

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Model:ES_01b_StudyAreaNoSeaSub‐models:

S_11_Add_BAP_Fields_V1, S_12_Add_LCM_Fields_V1, S_13_Add_DataSources_Fields_V1, S_z_All_Deletes_B1AX

(S_z_buffer_SA_removed, S_z_StudyArea_removed, S_z_Base01_removed, S_z_SA10_Sa50_removed,

S_z_B1AX_removed, S_z_RASTER_B1AX_removed) .

Main model:

Initially delete In Memory.

Calculate Value checks the status of the tick box “Use_Memory” and sets the workspace as either “In_Memory” or %Scratch%.

Sub‐models run to delete any data that may be present due to the model having previously run (old versions of the outputs).

o From “Scratch” any Feature Classes or Rasters containing *B1BX* are deleted. o From “Outputs” any Feature Classes with *buffer*, *study*, or *BaseMap01, or any Rasters with *SA* are

deleted.

Model takes the “MasterMap” data, Converts to feature layer, Selects only polygons > 0.7 m.

Add Fields “Area_m” and “Length_m” and calculate these from “Shape_area” and “Shape_length” (this is so that these fields are available for use when the data is used In_Memory, because the Shape fields are not present when the data is In_Memory).

Add and calculate field “Slvr_shp”. Calculate by (3.1415926535897932384626433832795 * (([Shape_Length] / (2 * 3.1415926535897932384626433832795)) ^ 2)) / [Shape_Area]. This is a shape index and allows the identification of small and narrow “sliver” polygons. Removes from selection polygons where Shape_area < 20 AND Slvr_shp > 15 AND NOT “DescGroup” = ‘Path’.

Copy to Scratch.

Analysis conducted to create a grid that covers the extent of the Study Area plus buffer.

This is so a polygon is created that comprises sub parts and is not one whole polygon of the StudyArea. This is because when carrying out select operations, these are substantially quicker when using a multiple polygon StudyArea file than a single polygon of the whole extent.

StudyArea1 – make feature layer.

Add field “cvt”, calculate to “1”. Convert to raster at 5,000m cells (based on cvt), raster to points, point to raster (based on value ID of each point), raster to polygon. Union the Studyarea1 with the grid polygons, repair geometry

Outputs created are: o StudyArea, SA_buffer, SA_buffer_grid, SA010, SA050, SA100.

Sub‐models add a range of data fields to MasterMap, as used by later analysis models (See data fields table)

Field DataOS Calculated with “OS” to label the resulting dataset as based on OS data source.




o copied to %Outputs% / BaseMap01. o copied to %Outputs% / BaseMap01b_%RunCode%_%outDate%.


Delete Tool run ‐ In Memory.

Model:ES_02_CollateVectorMapSub‐models:

S_z_B2X_removed, S_3WoodlandSub_V3, S_3WaterAreaSub_V3, S_3WaterLinesSub_V3,

S_3TidalWaterSub_V3, S_3Roadsub_V3, S_3RailstationsSub_V3, S_3RailwaysSub_V3,

S_3PublicAmentiesSub_V3, S_3HeritageSub_V3, S_3ForeshoreSub_v3, S_3ElectricityLinesSub_V3,

S_3BuildingsSub_V3, S_3AirportsSub_V3.

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Main model:

Sub model iterates through the Scratch workspace and selects and deletes any feature Classes with *B2X*.

This removes any old versions or previous analysis results.

A series of sub models are created, one for each output dataset. The sub models are set with Boolean “tick

box” pre‐conditions. This allows certain sub‐models to be de‐selected if necessary. Due to current

improvements in model run speeds, this is generally not necessary.

The models are designed so that they should run in any situation, even if data for a particular study area is

lacking. However if there are significant errors recurring within one sub‐model, or if it is known that the data

produced do not occur within the study area (e.g. no tidal areas) then particular sub models can be turned off.

This should be a last resort.

For each sub model the following steps occur:

o An iterator (iterate feature classes) checks through the VectorMap folder for the relevant data files

(from multiple tile folders) using a Wildcard selector, e.g. *Wood*.

o The output feature class files are collected as a parameter by “collect values” and passed to the main

model.

o A Get Count is conducted on each feature class as it passes through the iterator. The results of each

are collected by a “collect values” and set as a model parameter – passed to the main model. These

are named with a sub‐model specific file name e.g. “rows7”, “rows11” etc., etc.

In the main model, the outputs of each sub‐model have the following analysis steps:

o A “calculate value” tool with a python code block is used to check if any of the get counts from the

sub model have results. This is used to check for any sub‐models that are not producing any results,

e.g. empty selections. The calculate value is then set as a Boolean precondition to the merge process.

If no data are present then the merge does not execute.

o The collected feature classes (output values), from the iterator, are merged.

o The results are checked for outputs using a Get Count, set as a pre‐condition to the following.

o The results are then clipped to the study area buffer (SA_buffer). This reduces the amount of data /

files sizes.

o The data is located in the Outputs folder.

o Repair geometry is conducted on the results.

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Model:ES_03_AddOpenSpaceTypologytoBaseMap(OPTIONAL)Sub‐models:

S_z_Base3_removed, S_z_B3x_R_removed, S_z_B3X_removed, S_2CollectGIdata_V3,

S_0_Print_Message_V1.

Main model:

Sub‐models run to remove any old copies of data from previous analysis runs before the main analysis occurs.

o S_z_Base3_removed = iterates through the outputs folder and selects and deletes *BaseMap03_GI.

o S_z_B3x_R_removed = iterates through scratch folder and selects and deletes Raster files with *B3X.

o S_z_B3x_removed = iterates through scratch folder and selects and deletes Feature class files with *B3X.

Run sub‐model.

o S_2Collectdata_V3 = collects each separate county GI vector layer. Selects all GI data from the Inputs

folder based upon a selection Wildcard. Then iterates through them and uses a “Collect Values” to give an

output parameter, ready for use at the next stage.

Create feature class with British National Grid geographic coordinates. Add fields – typology and GI_coder

Append the above created Feature Class with the outputs of the Collect GI data sub‐model.

o There can be issues due to the expected data fields that may be present, therefore:

o Schema Type is set to NO_TEST. This results in any field in the input data that do not match the target

dataset will not be mapped to the target dataset. Therefore only the information from “Typology” and

“GI_coder” are copied across.

o Repair geometry.

o Multipart to single part to deal with potential problem multi part features.

o Select only the GI polygons present that intersect with SA_buffer_grid. Copy to scratch.

GI polygon data is converted to raster file at 2m cells, based on GI_coder. So that areas with no GI type are

properly considered, steps are processed to convert all Non Data into a code 8888 so that polygon that mostly

overlap with No Data or non OS / GI areas can be identified.

BaseMap01 is converted to feature layer, given attribute index, then copied to the In Memory and converted again

to a feature layer.

Fields are deleted in case they already exist.

BaseMap polygons that intersect the GI polygons are selected.

The selection of BaseMap polygons that intersect are then used as the Input to a Zonal Statistics, using the value of

the GIType2m2 raster. This returns information on the overlap of each polygon to the raster information on the

mapped OS / GI sites.

The Zone Field used to identify each polygon has caused problems in this analysis due to differences between

versions of ArcGIS. In ArcGIS 10.1 the Zone Field was set to “TOID” (a text / string field) however this causes errors

in 10.2.2 and this has subsequently been changed to use the field “T_ID” (a numeric long field).

The output – MMLA Typology table is saved In Memory to speed up the calculations.

Various setting are used for the Zonal statistics, e.g. snap raster and Cell size of 2m is set.

The output table (MMLATypology) is then joined to the BaseMap selection (the intersecting polygons with GI

merge) so that the results of the zonal stats are copied into the BaseMap table. The join field must match the Zone

field set in Zonal statistics (T_ID).

Attribute fields within the BaseMap are then updated based on the known overlaps to the OS / GI fields.

o Field “GI” is populated based on the code present in the field “majority”.

o Field “OST_var” is populated based on the code present in the field “variety”.

o Field GI_type (text) is populated from a script that looks up the Majority field (numeric value) and returns

a text string to the GI Type field based on numeric value.

Then new selection is made of the BaseMap layer – where GI Type = NULL Then the selection is populated with a

script, whereby if the area is MAKE = “Man made” then the GI Type is “Not Greenspace” and all other combinations

are “undetermined Greenspace”.

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Unnecessary fields are deleted (Variety and Majority).

Calculate field updates the field “Data03” with value 3 to indicate the BaseMap has been coded and updated using

available OS / GI data.


One file has a standard name and will always be named exactly as below. This is the file that is automatically

searched for and used by later models.

A second, identical copy is saved with the day's date and an optional user generated "RunCode" as part of the file

name. This file is for reference only. BaseMapiscopiedto. o %Outputs%\BaseMap03_%RunCode%_GI_%outDate%.

o %Outputs%\BaseMap03_GI.

Finally the In Memory space is deleted.

The following attribute categories are used in this model.

AccessGI Accessibilitylevel1 Accessible

0 Nopublicaccess

Typologyattributenames

GI_Coder Definition

Allotmentorcommunityfarm

1 Landprovidedforcultivationandvoluntaryactivities.

AmenityGreenspace

2 Village greens and Greenspace in and around housing that is public and doesn’t have aspecificfunction.Typicallythisisshortmownamenitygrassland.

Cemetery 3 Churchyards,cemeteries&otherburialgrounds.Coastal 4 Beachesandopenaccessintertidalareas.NaturalandSemi‐naturalGreenspace

5 Variousnatural or semi‐natural areas consideredGreenspaceby local authority surveys.Thesemayincludemeadows,riversides,woodlandsetc.These should be publicly accessible. If no information is available, or it is unclear if apolygonispubliclyaccessiblethenthesesitesshouldnotbeincluded.

Parkorpublicgarden

6 Formal and informal urban parks, country parks, and formal gardens. These should bepubliclyaccessible–notchargeforaccess.

Playfacilities 7 Areas equipped for children and teenagers to play and socialise (e.g. youth shelters andplaygrounds).

Sportsfacilities 8 Tennis courts, bowling greens, sports pitches, golf courses, athletics tracks, school andotherinstitutionalplayingfields,andotheroutdoorsportsareaswithnaturalorartificialsurfaces.Eitherpubliclyorprivatelyowned.

Mixed 9 MixedcategorieswhereitisunclearwhichisthemainGreenspacetype.Includethisonlyiftheseareashavedefinitelybeenidentifiedaspubliclyaccessibleopenspace.

Accessiblewoodland

10 Anyareasofpubliclyaccessiblewoodland.Notethismaybecommunitywoodlandetc.andrecently plantedwoodland, so itmight not have beenmapped in theNatural and Semi‐Natural Greenspace category. If these areas are mapped as Natural and Semi‐NaturalGreenspacetheyneednotbeduplicatedhere.

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Model:ES_04_AddLCATypologytoBaseMap(OPTIONAL)Sub‐Models:

S_z_B4AX_removed, S_z_B4AX_R_removed, S_z_Base4a_removed, S_4Zonal_1_V50 (inc Zonal

4_subAddCalcFields_V2), S_0_Print_Message_V1.

Sub‐Models:

Initial sub‐models delete any existing data that may be present following previous runs of the Toolkit.

Takes LCA data as input, selects data that intersects with SA_buffer and saves as new copy to Scratch –

LCA_in_SA_buffer.

LCA is converted to a raster at 5m cells (extent, snap raster set) – based on field = LCA_coder.

So that areas of unknown LCA type are properly considered, steps are processed to convert all non‐Data into a

code 8888 so that polygons that mostly overlap with NoData or non‐mapped LCA areas can be identified.

Raster is in Scratch – LCAType5m. Then passed to the sub‐model.

BaseMap runs Delete Field to delete a set number of fields, if they are present. BaseMap copied to Scratch –

BM_LCA_zones_input.

BaseMap is converted to feature layer, given attribute index. Then passed to the sub‐model.

Sub‐model – S_4Zonal_1_V50.

This sub‐model runs a series of analysis. The aim is to code the BaseMap polygons by the main overlap with the

LCA raster data.

The model uses zonal statistics. Because Zonal statistics does not work well (or is not appropriate) for small or

linear polygons separate analysis is applied to these polygons to derive a representative value from the LCA data.

Small polygons are selected where Area_m <= 250.

Run Feature to points (in_memory). Extract value to points (in_memory).

Table to table then copies the resulting Points with values output table to in_memory – Points_tables.

Field Mapping makes sure to retain both TOID and RASTERVALUE.

(At selected stages the in_memory files are deleted, to free up space).

Passes output to Sub‐model – Zonal_3_subAddCalcFields_V2.

Sub‐model – Zonal 4_subAddCalcFields_V2.

Adds and calculates fields. RV_int (long) = raster value (this is created so there is a long (integer) version of the

raster value (needed because some calculations will not work with float point values),

Min, max, Range, Mean, STD, Sum (float). Variety, Majority, Minority, Median (Long).

Calculate fields, all = RASTERVALUE, except Range = 0, STD = 0, Variety = 1, Majority = RV_int, Minority = RV_int,

Median = RV_int.

Results are copied to Scratch – extract_val_rows_plus (for testing, viewing, not used by the model)

Selects larger polygons Area_m > 250.

A series of selections, based on patch size are then used to send a number of polygons to several zonal statistics

tools. This is so that any limitations on the number of polygons able to be processed are avoided.

The results are then merged.

Outputs from Zonal Stats –are merged together (using the sub model collect).

Two main outputs are merged – Points tables (results for smaller polygons) and Zonal_merge (main results from

the zonal stats).

The original BaseMap input from the main model is copied to In_memory – Basemap_GI_MemJoin.

The file with joins is then set as a parameter – to allows its use back in the main model (still in memory).

Main model:

In main model – uses the joined file In_memory, Calculates fields based on the min, max, variety data etc.

LCA_var = variety, LCA_code = majority, , LCA_type (reclass based on the majority field),

Made into a feature layer, Clear selection, to make sure no selections are present.

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Delete field is used to delete all the original min, max, variety etc. fields.

Field Data04 is calculated as "4" to indicate the data has been classified by this model.





name. This file is for reference only.

o copied to %Outputs% / BaseMap04_LCA

o copied to %Outputs% / BaseMap04_LCA_%RunCode%_%outDate%

Several "Print message" sub‐models are run, based on selections from the main output file. These select and then

count the number of NULL value polygons within certain fields to indicate if there may have been problems with

running the models. Messages are printed in the model run window.


LCA codes for reference. See User Guide for details on the preparation of input data.

LCA_habita LCACoder Description

Allotments 1 LandprovidedforcultivationandvoluntaryactivitiesAmenitygrassland

2 Short, improvedgrasslandusedwhichmaybeused for recreationor to fill spacebetweenhousingandgreyinfrastructure(e.g.roadverges).Mayalsoincludesportsfacilities.

Arable 3 AreasusedforgrowingcropsCemetery 4 Churchyards,cemeteries&otherburialgrounds

Pasture 5 AreasusedforgrazinganimalsGolfcourse 6 AreasdedicatedtoplayinggolfHeath/Moors 7 Foundmainlyonacidicsoils,characterizedbyopen,lowgrowingvegetationIndustry 8 AnyareasdedicatedtoindustryorotherfactoriesorworksQuarry/Mineral 9 ActiveordisusedquarriesandothermineralworkingsLandfill 10 ActivelandfillsitesMixedfarmland 11 AreascontainingbothpastureandcropsParks andgardens

12 Formalandinformalurbanparks,countryparks,andformalgardens

Caravan 13 Caravansitesortravellerssites

Roughgrassland

14 Grasslandintheuplandsorlowlandsthattendsnottobehighlyimproved.Inthelowlandsthesemaynotberegularlymanaged.Intheuplandstheseareoftenlargeracidgrasslandthatareoftenunenclosed.

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Model:ES_05_AddBAPHabitattoBaseMap(OPTIONAL)Sub‐Models:

S_z_Base4b_removed, S_z_B4BX_R_removed, S_z_B4BX_removed.

Main model:

Initial sub‐models run to delete any existing copies of results from previous runs of the model.

Copy input BaseMap to Scratch. BaseMap – copied to Scratch, converted to Feature Layer.

BAP_Code – make feature layer, select where intersects SA_buffer. Copy features to Scratch, field added (Const1),

converted to Raster, based on Const1, 2m cells (snap, extent = SA010 / SA050) = BAP_raster.

LWS ‐ make feature layer, select where intersects SA_buffer. Copy features to Scratch, field added (Const1),

converted to Raster, based on Const1, 2m cells (snap, extent = SA010 / SA050) = LWS_raster.

Zonal stats – Input = those BaseMap polygons that overlap with any BAP_code polygons. Raster = BAP_raster. 2m

cells analysis size. Output = Zonal_BAP.

Zonal stats – Input = those BaseMap polygons that overlap with any LWS sites. Raster = LWS_raster. 2m cells

analysis size. Output = Zonal_LWS.

BaseMap. Join fields from Zonal stats (LWS) to the BaseMap in Scratch.

Add field ‐ LWS_p. Calculate field – SUM*4 / Area_m. Delete SUM field from BaseMap.

Join fields from Zonal stats (BAP) to the BaseMap in Scratch.

Add field ‐ BAP_p. Calculate field – SUM*4 / Area_m. Delete SUM field from BaseMap.

Tabulate areas. Input – Feature ZONE data = BaseMap, TOID = Zone field. Class data = BAP_code_Copy, BAPCode =

Class field. Output = BAP_areas. Processing size = 5 m.

This tool takes unique zones (TOID polygons) and calculates the area of each class that occurs in the polygon. In the

output table a new data Field is added for each Class, and this is filled with the area of each type that occurs in

each polygon.

Join field add all the BAP data fields to the BaseMap data.

BaseMap checks. A number of fields are added – each to show the proportion of habitat type (BAP).

A field check script then runs to check if the field is present, if it is then calculate field runs and calculates the

proportion (0 to 1) of polygon covered by each habitat type (BAP / Area_m).

A collect values is used to then collate the results. The collect values is set with precondition so that the following

run after the previous analysis has run: Delete field is run to delete all the BAP fields, B1,B2,B3,B4 etc.

Copy features runs to copy the resulting file to Outputs.






name. This file is for reference only.

o copied to %Outputs% / BaseMap05_BAP

o copied to %Outputs% / BaseMap05_BAP_%RunCode%_%outDate%

The “RunCode” is set to allow each run of the Models to be coded by a StudyArea and / or run specific code Delete Tool run ‐ In Memory.

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Model:ES_06_AddSlopestoBaseMap(OPTIONAL)Sub‐Models:

S_z_Base6_slopes_TABLES_removed, S_z_Base6_slopes_removed, S_z_B4CX_R_removed,

S_z_B4CX_removed, S_4Zonal_1_V53x (S_4Zonal_Collect_tables_V2), S_0_Print_Message_V1.

Sub‐Models:

Initial sub‐models run to delete an existing copies of results from previous runs of the model.


DTM is copied to scratch ‐ only for the extent of the StudyArea. Slope layer created, then converted to integer.

Sub‐model – S_4Zonal_1_V53x: This sub‐model runs a series of analysis. The aim is to code the BaseMap polygons

by the slopes data. The model uses zonal statistics. Because Zonal statistics does not work well (or is not

appropriate) for small or linear polygons separate analysis is applied to these polygons to derive a representative

value from the LCA data.

Small polygons are selected where Area_m <= 250. Run Feature to points (in_memory). Extract value to points

(in_memory). Table to table then copies the resulting Points with values output table to in_memory –

Points_tables. Field Mapping makes sure to retain both TOID and RASTERVALUE.



Sub‐model – Zonal 4_subAddCalcFields_V2: Adds and calculates fields. RV_int (long) = raster value (this is created

so there is a long (integer) version of the raster value (needed because some calculations will not work with float

point values). Min, max, Range, Mean, STD, Sum (float). Variety, Majority, Minority, Median (Long)

Calculate fields, all = RASTERVALUE, except Range = 0, STD = 0, Variety = 1, Majority = RV_int, Minority = RV_int,

Median = RV_int.

Selects larger polygons Area_m > 250: A series of selections based on patch size and then used to send a number of

polygons to several zonal statistics tools. This is so that any limitations on the number of polygons able to be

processed are avoided.

The results are then merged.



the zonal stats).



Main Model:

In main model – uses the joined file In_memory.

Calculates fields based on the min, max, variety data etc. Slope_mean, Slope_min, Slope_max and Slope_range are

then populated.


Delete field is used to delete all the original min, max, variety etc fields.





o copied to %Outputs% / BaseMap06_Slopes. o copied to %Outputs% / BaseMap06_Slopes_%RunCode%_%outDate%.


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Model:ES_07Add_ElevationtoBaseMap(OPTIONAL)Sub‐Models:

S_z_Base4d_removed, S_z_B4DX_R_removed, S_z_B4DX_removed, S_4Zonal_1_V50

(S_4Zonal_Collect_tables_V2), S_0_Print_Message_V1.

Main model:



DTM is copied to scratch ‐ only for the extent of the StudyArea.

Sub‐model – S_4Zonal_1_V53x: This sub‐model runs a series of analysis. The aim is to code the BaseMap polygons

by the elevation data. The model uses zonal statistics. Because Zonal statistics does not work well (or is not

appropriate) for small or linear polygons separate analysis is applied to these polygons to derive a representative

value from the DTM data.

Small polygons are selected where Area_m <= 250: Run Feature to points (in_memory). Extract value to points

(in_memory). Table to table then copies the resulting Points with values output table to in_memory –

Points_tables. Field Mapping makes sure to retain both TOID and RASTERVALUE.



Sub‐model – Zonal 4_subAddCalcFields_V2: Adds and calculates fields. RV_int (long) = raster value (this is created

so there is a long (integer) version of the raster value (needed because some calculations will not work with float

point values), Min, max, Range, Mean, STD, Sum (float) Variety, Majority, Minority, Median (Long). Calculate

fields, all = RASTERVALUE, except Range = 0, STD = 0, Variety = 1, Majority = RV_int, Minority = RV_int, Median =

RV_int.

Selects larger polygons Area_m > 250: A series of selections based on patch size and then used to send a number of

polygons to several zonal statistics tools. This is so that any limitations on the number of polygons able to be

processed are avoided. The results are then merged.



the zonal stats).



In main model – uses the joined file In_memory.

Calculates fields based on the min, max, variety data etc. Elev_mean, Elev_min, Elev_max and Elev_range are then

populated.


Delete field is used to delete all the original min, max, variety etc fields.





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o copied to %Outputs% / BaseMap07_Elevation. o copied to %Outputs% / BaseMap07_Elevation_%RunCode%_%outDate%.






Model:ES_08AddWoodlandSurveytoBaseMap(Scotland)(OPTIONAL)Sub‐Models:

S_z_ALL_Deletes_BSNW (S_z_BaseSNW_removed, S_z_BaseSNW_RASTER_removed,

S_z_BaseSNW_FEATURES_removed), S_z_B4DX_R_removed, S_z_B4DX_removed, S_4Zonal_1_V50

(S_4Zonal_Collect_tables_V2), S_0_Print_Message_V1.

Main model:


FC_WSS data, runs repair geometry, then selects and saves a copy of all polygons in the Study Area, copy to scratch

Polygons with > the set %Semi_natural_cover% threshold are selected then , calculate a new field of ”1” and use

this to convert to raster data.

Replace NULLS with 8888.

BaseMap polygons that intersect the FC_NWSS are selected and used to run a Zonal statistics.

Variety and Majority fields are added back to BaseMap.

Field “FC_SN_Woods” is populated from the majority field.

All polygons not coded as semi‐natural are set to 8888.

Majority and Variety fields are deleted.

Calculate field – “08” added in Field “Data08” to indicate the data layer has been updated with Woodland survey

data.




o copied to %Outputs% / BaseMap08_SNW. o copied to %Outputs% / BaseMap08_SNW_%RunCode%_%outDate%.



Model:ES_09Add_LCMdatatoBaseMap (OPTIONAL)Sub‐Models:

S_z_Base_LCM_outputs_removed, S_z_Base_LCM_scratch_rasters_removed,

S_z_Base_LCM_scratch_features_removed.

Main model:



LCM data copied to scratch, with an extent set by current Study Area.

Tabulate area used to define the area of each LCM habitat type present within each polygon.

Resulting fields joined to the BaseMap.

Series of Field Check scripts used to see which fields have been added and are present.

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Calculate field used to determine the proportion of the polygon covered by each LCM habitat type.





o copied to %Outputs% / BaseMap09_LCM. o copied to %Outputs% / BaseMap09_LCM_%RunCode%_%outDate%.

The “RunCode” is set to allow each run of the Models to be coded by a StudyArea and / or run‐specific code


Model:ES_10Add_UrbanandAWItoBaseMapSub‐Models:

S_z_Base4e_removed, S_z_B4EX_R_removed, S_z_B4EX_removed.

Main model:



AWI data are copied to scratch, only for the areas present within the StudyArea.

Then converted to raster layers at 5 m cells.

Then zonal statistics conducted, at 5 m. The resulting Area and Sum fields joined back to the data.

Proportion of AW area calculated into field "AWIa".

Urban layer selected, all polygons within the urban polygons then calculated in the "Urb" attribute field.





o copied to %Outputs% / BaseMap10_Urban. o copied to %Outputs% / BaseMap10_Urban_%RunCode%_%outDate%.



Model:ES_11ClassifyallBaseMaphabitatsSub‐Models:

S_z_B5X_removed, S_z_Base9_removed, S_0_Print_Message_V1.

Sub‐Models:

The model is designed as a series of reclassifications. Each reclassification updates the final habitat type of each

polygon based on a range of information present in different attribute fields. The model will run even if all information

is not present within all the data fields. However some reclassifications will only update the final habitat type if certain

data is present. Therefore the reclassification will produce different results depending on which other models have

already been run. The reclassification run in sequence, gradually updating each habitat field and then updating it

further if more detailed information is available.

A range of standalone variables are set as parameters, allowing user defined entry, but with default values.

15 | P a g e

These area: LCM_min, Montane, AWIoverlap, Urban Overlap, Dry slopes, Semi‐improved slopes, Unimproved

slopes, Improved_max, Arable_min, BAP_max, BAP_min, Houses_max, Garden_shape, Garden_max, Houses_min.

In_memory is deleted.

BaseMap copied to In_memory, with pre‐condition set to Delete Fields to ensure there is no conflict with later add

and calculate fields processes. Make feature layer.

Calculate field – sets HabCode field to Null / None.

HabCode – Reclass – Group 1 ( several FINAL or high certainty categories ), Group 2 Roads and Manmade (roads,

manmade ), Group 3 Gardens, Group 4 Buildings, Group 5 Buildings.

Select gardens, Select Buildings if Area_m >30 and < 800.

Subset selection select Buildings within 5m of Garden – these are then re‐classified as Domestic buildings

HabCode – Reclass – 6 Woodlands, 7 Grasslands, 8 Wetlands / heaths, Trees and woods habitats, unclassified

Add field – BAP_sum . Calculate – add together all the separate BAP proportion fields to give the total BAP cover

per polygon, Add field – BAP_type2, Add field – HabCode_B.

BAP 1,2,3 ‐ BAP_type2 – reclass – BAP 1, BAP2, BAP3 (examines each BAP category field in turn. If the habitat

specific field has greater than 0.6 present, then the appropriate code is returned in the field BAP_Type2)

Calculate field – HabCode_B = HabCode.

HabCode_B – reclass: A series of reclass operation are carried out to alter the Habitat code in HabCode_B

depending on what codes are present in BAP_Type2.

Habitat + BAP – F1, F2,F3, Open Space, LCA landscape, Linear, LCM, Area and Shape, Slopes, Urban, HabCode_B,

AWI HabNat, Montane, GI_Type, FC Scot woods, Scattered trees .

Field DataSources is calculated as "DataOS + Data03 + Data04 + Data05 + Data06 + Data07 + Data08 + Data09 +

Data10 + data11 " to indicate the data that has contributed to this particular BaseMap version.


One file has a standard name and will always be named exactly as below. This is the file that is automatically. searched for and used by later models.


o copied to %Outputs% / BaseMap11_Habitats. o copied to %Outputs% / BaseMap11_Habitats_%RunCode%_%outDate%.






The following tables illustrate the data types used in the creation of the final habitat types.

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Detailed GIS steps used within model ES 11 Classify BaseMap habitats (Mainly reclassification of fields: HabCode, HabCode_B, and GI_type)

Reclassgroup

Fieldupdated

Attributeassigned

Selection/Calculation#defaultvalues,whererelevant#

1General HabCode G3 DescGroup==“Sea”1General HabCode G 'InlandWater’inDescGroup1General HabCode J363 ‘Structure'inDescGroup1General HabCode J364 DescGroup==‘Glasshouse’1General HabCode A112o DescTerm==‘Orchard’1General HabCode I12 DescTerm==‘Scree’1General HabCode I14b DescTerm==‘Boulders’orDescTerm==‘Boulders;Rock’orDescTerm==‘Boulders(Scattered)1General HabCode G26 DescGroup==‘TidalWater’andDescTerm!=‘Foreshore’1General HabCode H1u DescTerm==‘Foreshore’1General HabCode I1 DescTerm==‘Rock’orDescTerm==‘Rock(Scattered)’2ManMade HabCode J512 DescGroup==‘RoadOrTrack’andMake==‘Natural’2ManMade HabCode J511 DescGroup == 'Road Or Track' andMake == ‘Manmade’ OR ‘Road Or Track’ in DescGroup and Make ==

‘Manmade’2ManMade HabCode J37 ‘General surface’ in DescGroup and Make == ‘Manmade’ OR General surface’ in DescGroup and Make ==

‘Unknown’2ManMade HabCode J12v ‘Roadside’inDescGroupandMake==‘Natural’2ManMade HabCode J52 Roadside’inDescGroupandMake==‘Manmade’orRoadside’inDescGroupandMake==‘Unknown’2ManMade HabCode J53 RailinDescGroupandMake!=‘Natural’2ManMade HabCode J54 PathinDescGroupandMake==‘Manmade’3Gardens HabCode J56 ‘Generalsurface’inDescGroupandMake==‘Multiple’andArea_m<=%Garden_max%#Garden_max=800#3Gardens HabCode J56 ‘General surface’ in DescGroup and Make == ‘multiple’ and Shape_index <= %Garden_shape%

#Garden_shape=10#3Gardens HabCode J55 ‘Generalsurface’inDescGroupandMake==‘Multiple’andArea_m>%Garden_max%andnotShape_index<=

%Garden_shape%#Garden_shape=10#4Buildings HabCode J36u ‘Building’inDescGroup5Buildings HabCode J361 ‘Building’inDescGroupandArea_m>%House_max%#%House_max%=800#5Buildings HabCode J362 ‘Building’inDescGroupandArea_m<%House_min%#%House_min%=30#5Buildings HabCode J360 DescGroupLIKE%Building%andArea_m>%House_min%andArea_m<%Houses_max%andlessthan5m

fromJ56(privategardens)6Woodlands HabCode A11 DescTerm==‘NonconiferousTrees’6Woodlands HabCode A11/A2 (DescTerm ==’Nonconiferous Trees; Scrub)’ OR ‘Scrub; Nonconiferous Trees’ OR ‘Nonconiferous

Trees;Scrub’(nospaces)OR‘Scrub;NonconiferousTrees’OR‘Scrub;NonconiferousTreesor‘CoppiceOrOsiers;NonconiferousTrees’orCoppiceOrOsiers;NonconiferousTrees;Scrub

6Woodlands HabCode A12 DescTerm='ConiferousTrees'6Woodlands HabCode A12/A2 DescTerm='ConiferousTrees;Scrub'OR'ConiferousTrees;Scrub'or‘Scrub;ConiferousTrees’6Woodlands HabCode A13 DescTerm=‘NonconiferousTrees;ConiferousTrees’OR‘ConiferousTrees;NonconiferousTrees’6Woodlands HabCode A2 DescTerm==‘Scrub’orCoppiceOrOsiers6Woodlands HabCode A31 DescTerm=‘NonconiferousTrees(Scattered)’6Woodlands HabCode A32 DescTerm=‘ConiferousTrees(Scattered)’6Woodlands HabCode A31/A2 DescTerm==‘NonconiferousTrees(Scattered);Scrub+othercombinations’

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Reclassgroup

Fieldupdated

Attributeassigned


6Woodlands HabCode A33 DescTerm == 'Coniferous Trees (Scattered); Nonconiferous Trees (Scattered)' OR 'Nonconiferous Trees(Scattered);ConiferousTrees(Scattered)'

6Woodlands HabCode A13/A2 DescTerm== 'ConiferousTrees;NonconiferousTrees;Scrub' 'ConiferousTrees;NonconiferousTrees;Scrub'OR 'Coniferous Trees; Coppice Or Osiers; Nonconiferous Trees' OR 'Nonconiferous Trees;ConiferousTrees;Scrub' /nOR 'Coniferous Trees; Coppice Or Osiers; Nonconiferous Trees; Scrub' OR'Scrub;Nonconiferous Trees;Coniferous Trees' OR 'Nonconiferous Trees;Scrub;Coniferous Trees' OR'ConiferousTrees;Scrub;NonconiferousTrees'OR'Scrub;ConiferousTrees;NonconiferousTrees')

6Woodlands HabCode A31/A2 DescTerm == Scrub; Nonconiferous Trees (Scattered)’ or DescTerm = Nonconiferous Trees (Scattered);Scrub)

7Grasslands HabCode B4/J11 'GeneralSurface'inDescGroupandMake==‘Natural’7Grasslands HabCode Bu DescTerm==‘Roughgrassland’7Grasslands HabCode Bu1 DescTerm==Boulders(Scatter);Roughgrassland’or‘Rock(Scattered);RoughGrassland’ OR‘Rock;Rough

Grassland’ or DescTerm == ‘Rough Grassland; Rock (Scattered)’ or ‘Rough Grassland; Heath; Rock(scattered) or DescTerm == ‘ Boulders (Scattered); Rock (Scattered); Rough Grassland’ DescTerm == ‘Boulders; Rock (Scattered); Rough Grassland’ or DescTerm == ‘Boulders; Heath; Rough Grassland’ orDescTerm == ‘Rough Grassland; Heath’ or DescTerm == ‘Heath; Rock (Scattered); Rough Grassland’ orDescTerm==‘RoughGrassland;Boulders’orDescTerm==‘Boulders;RoughGrassland’

7Grasslands HabCode Bu_A2/A31 DescTerm==‘RoughGrassland;Scrub’OR‘RoughGrassland;Scrub’or DescTerm==‘NonconiferousTrees(Scattered),RoughGrassland’ORDescTerm==‘NonconiferousTrees(Scattered),RoughGrassland;Scrub’

7Grasslands HabCode Bu_A11 DescTerm== ‘Nonconiferous trees;RoughGrassland;Scrub’ORDescTerm== ‘Nonconiferous trees;RoughGrassland’

7Grasslands HabCode Bu_A12 DescTerm==‘Coniferoustrees;RoughGrassland’7Grasslands HabCode Buu/C31 DescTerm='RailandMake==Natural’'8Wetlands+Heaths HabCode B5/E3/F/H2 DescTerm==‘MarshReedsORSaltmarsh’8Wetlands+Heaths HabCode D/E DescTerm==‘Heath’8Wetlands+Heaths HabCode D/I DescTerm=='Boulders(Scattered);Heath'OR'Boulders;Heath;Rock'OR'Boulders;Heath'OR'Heath;Rock

(Scattered)'8Wetlands+Heaths HabCode D5/D6 DescTerm=='Heath;MarshReedsOrSaltmarsh;RoughGrassland'orDescTerm=='Heath;RoughGrassland;

MarshReedsOrSaltmarsh'8Wetlands+Heaths HabCode D_B5/E3/F/H2 DescTerm==‘'Heath;MarshReedsOrSaltmarsh'8Wetlands+Heaths HabCode D5 DescTerm==‘Heath;RoughGrassland’OR‘Boulders(Scattered);Heath;RoughGrassland’orDescTerm==

‘Heath;RoughGrassland;Rock(Scattered)’8Wetlands+Heaths HabCode B5/E3/F/H2_Bu DescTerm=='MarshReedsOrSaltmarsh;RoughGrassland'orDescTerm=='RoughGrassland;MarshReeds

OrSaltmarsh'Trees+WoodsPLUS HabCode D5_Bu_Au HabCode==Noneand‘Heath’inDescTermand‘RoughGrassland’inDescTermand‘Trees’inDescTermor

HabCode==Noneand‘Heath’inDescTermand‘RoughGrassland’inDescTermand‘Scrub’inDescTermTrees+WoodsPLUS HabCode D5_Au HabCode ==Noneand ‘Heath’ inDescTermand ‘Trees’ inDescTermor HabCode ==Noneand ‘Heath’ in

DescTermand‘Scrub’inDescTermTrees+WoodsPLUS HabCode Bu_Au HabCode==Noneand‘’Roughgrassland’inDescTermand‘Trees’inDescTermorHabCode==Noneand

‘Rough grassland’ in DescTerm and ‘Scrub’ in DescTerm or HabCode ==None and ‘Rough grassland’ inDescTermand‘Coppice’inDescTerm

Unclassified HabCode Unclassified DescGroup==‘Unclassified’

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Reclassgroup

Fieldupdated

Attributeassigned


SumofBAP BAP_sum number [Grass_p] + [RBog_p] + [BBog_p] + [Calc_p] + [Decwds_p] + [Purple_p] + [Lagn_p] + [Salt_p] + [Park_p] +[Meadow_p]+[Brown_p]+[Fens_p]+[Mud_p]+[Reed_p]+[Cliff_p]+[Mosaic_p]+[Flood_p]+[Undt_p]+[Dune_p]+[SNwds_p]+[Acid_p]+[Shingle_p]+[Heath_p]+[Pave_p]+[Orch_p]+[Grass_m_p]+[GQSI_p]+[Up_p]+[MScrub_p]

BAP1 BAP_type2 Bu1 Grass_p>%BAP_min%#BAP_min=0.6#BAP1 BAP_type2 E162 RBog_p>%BAP_min%#BAP_min=0.6#BAP1 BAP_type2 E1u BBog_p>%BAP_min%#BAP_min=0.6#BAP1 BAP_type2 B31 Calc_p>%BAP_min%#BAP_min=0.6#BAP1 BAP_type2 A1u Decwds_p>%BAP_min%#BAP_min=0.6#BAP1 BAP_type2 B5 Purple_p>%BAP_min%#BAP_min=0.6#BAP1 BAP_type2 G16 Lagn_p>%BAP_min%#BAP_min=0.6#BAP1 BAP_type2 H2u Salt_p>%BAP_min%#BAP_min=0.6#BAP1 BAP_type2 A3p Park_p>%BAP_min%#BAP_min=0.6#BAP1 BAP_type2 B21 Meadow_p>%BAP_min%#BAP_min=0.6#BAP1 BAP_type2 J13 Brown_p>%BAP_min%#BAP_min=0.6#BAP1 BAP_type2 E3/F1 Mud_p>%BAP_min%#BAP_min=0.6#BAP1 BAP_type2 H11 Fens_p>%BAP_min%#BAP_min=0.6#BAP1 BAP_type2 F1 Reed_p>%BAP_min%#BAP_min=0.6#BAP1 BAP_type2 H8 Cliff_p>%BAP_min%#BAP_min=0.6#BAP2 BAP_type2 D5/D6 Mosaic_p%BAP_min%#BAP_min=0.6#BAP2 BAP_type2 B4f Flood_p%BAP_min%#BAP_min=0.6#BAP2 BAP_type2 Buu Undt_p%BAP_min%#BAP_min=0.6#BAP2 BAP_type2 H6u Dune_p%BAP_min%#BAP_min=0.6#BAP2 BAP_type2 A111 SNwds_p%BAP_min%#BAP_min=0.6#BAP2 BAP_type2 B11 Acid_p%BAP_min%#BAP_min=0.6#BAP2 BAP_type2 H3/H5 Shingle_p%BAP_min%#BAP_min=0.6#BAP2 BAP_type2 Du Heath_p%BAP_min%#BAP_min=0.6#BAP2 BAP_type2 I13 Pave_p%BAP_min%#BAP_min=0.6#BAP2 BAP_type2 A112o Orch_p%BAP_min%#BAP_min=0.6#BAP2 BAP_type2 B11m Grass_m_p%BAP_min%#BAP_min=0.6#BAP2 BAP_type2 Bu2 GQSI_p%BAP_min%#BAP_min=0.6#BAP3 BAP_type2 E2/E3/F1 Up_p%BAP_min%#BAP_min=0.6#BAP3 BAP_type2 A2m MScrub_p%BAP_min%#BAP_min=0.6#HabCode_B HabCode_B various !HabCode!BAP–F1 HabCode_B A111 ‘A’inHabCode_BandBAP_type2==‘A111’BAP–F1 HabCode_B A112o ‘A’inHabCode_BandBAP_type2==‘A112o’BAP–F1 HabCode_B A2m ‘A’inHabCode_BandBAP_type2==‘A2m’BAP–F1 HabCode_B A2m HabCode_B=='B4/J11'andBAP_type2==‘A2m’BAP–F1 HabCode_B A2m 'D'inHabCode_BandBAP_type2==‘A2m’BAP–F1 HabCode_B A3p 'A2'inHabCode_BandBAP_type2==‘A3p’BAP–F1 HabCode_B A3p 'A3'inHabCode_BandBAP_type2==‘A3p’BAP–F1 HabCode_B A3p 'B'inHabCode_BandBAP_type2==‘A3p’BAP–F1 HabCode_B B11m 'B'inHabCode_Band'A'notinHabCode_BandBAP_type2==‘B11m’

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Reclassgroup

Fieldupdated

Attributeassigned


BAP–F1 HabCode_B B11m 'D'inHabCode_Band'A'notinHabCode_BandBAP_type2==‘B11m’BAP–F1 HabCode_B B11 'B'inHabCode_Band'A'notinHabCode_BandBAP_type2==‘B11’BAP–F1 HabCode_B B11 'D'inHabCode_Band'A'notinHabCode_BandBAP_type2==‘B11’BAP–F2 HabCode_B B21 'B'inHabCode_Band'A'notinHabCode_BandBAP_type2==‘B21’BAP–F2 HabCode_B Bu1 'B'inHabCode_Band'A'notinHabCode_BandBAP_type2==‘Bu1’BAP–F2 HabCode_B Bu1 'D'inHabCode_Band'A'notinHabCode_BandBAP_type2==‘Bu1’BAP–F2 HabCode_B Bu2 'B'inHabCode_Band'A'notinHabCode_BandBAP_type2==‘Bu2’BAP–F2 HabCode_B Bu1 'D'inHabCode_Band'A'notinHabCode_BandBAP_type2==‘Bu2’BAP–F2 HabCode_B B4/J11 HabCode_B==‘B4/J11’andBAP_type2==‘Buu’BAP–F2 HabCode_B B31 'B'inHabCode_Band'A'notinHabCode_BandBAP_type2==‘B31’BAP–F2 HabCode_B B31 'D'inHabCode_Band'A'notinHabCode_BandBAP_type2==‘B31’BAP–F2 HabCode_B B4f HabCode_B==‘B4/J11’andBAP_type2==‘B4f’BAP–F2 HabCode_B B4f 'B'inHabCode_Band'A'notinHabCode_BandBAP_type2==‘B4f’BAP–F2 HabCode_B B5 'B'inHabCode_Band'A'notinHabCode_BandBAP_type2==‘B5’BAP–F2 HabCode_B B5 'D'inHabCode_Band'A'notinHabCode_BandBAP_type2==‘B5’BAP–F2 HabCode_B Du 'B'inHabCode_Band'A'notinHabCode_BandBAP_type2==‘Du’BAP–F2 HabCode_B Du HabCode_B==‘D/E’andBAP_type2==‘Du’BAP–F2 HabCode_B Du HabCode_B==‘D_B5/E3/F/H2’andBAP_type2==‘Du’BAP–F2 HabCode_B D5/D6 'B'inHabCode_Band'A'notinHabCode_BandBAP_type2==‘D5/D6’BAP–F2 HabCode_B D5/D6 HabCode_B==‘D/E’andBAP_type2==‘D5/D6’BAP–F2 HabCode_B D5/D6 HabCode_B==‘D_B5/E3/F/H2’andBAP_type2==‘D5/D6’BAP–F2 HabCode_B E2/E3/F1 'B'inHabCode_Band'A'notinHabCode_BandBAP_type2==‘E2/E3/F1’BAP–F2 HabCode_B E2/E3/F1 'D'inHabCode_Band'A'notinHabCode_BandBAP_type2==‘E2/E3/F1’BAP–F2 HabCode_B E3/F1 'B'inHabCode_Band'A'notinHabCode_BandBAP_type2==‘E3/F1’BAP–F2 HabCode_B E3/F1 'D'inHabCode_Band'A'notinHabCode_BandBAP_type2==‘E3/F1’BAP–F2 HabCode_B E1u HabCode_B==‘Bu1/Bu2’andBAP_type2==‘E1u’BAP–F2 HabCode_B E1u HabCode_B==‘Bu’andBAP_type2==‘E1u’BAP–F2 HabCode_B E1u HabCode_B==‘B4/J11’andBAP_type2==‘E1u’BAP–F2 HabCode_B E1u HabCode_B==‘B5/E3/F/H3’andBAP_type2==‘E1u’BAP–F2 HabCode_B E1u HabCode_B==‘B5/E3/F/H3_Bu1/Bu2’andBAP_type2==‘E1u’BAP–F2 HabCode_B E1u 'D'inHabCode_Band'A'notinHabCode_BandBAP_type2==‘E1u’BAP–F2 HabCode_B E162 'B'inHabCode_BandBAP_type2==‘E162’BAP–F2 HabCode_B E162 'D'inHabCode_BandBAP_type2==‘E162’BAP–F2 HabCode_B F1 'B'inHabCode_BandBAP_type2==‘F1’BAP–F2 HabCode_B F1 'D'inHabCode_BandBAP_type2==‘F1’BAP–F3 HabCode_B G16 HabCode==‘G’andBAP_type2==‘G16’BAP–F3 HabCode_B H6u ‘B’inHabCodeandBAP_type2==‘H6u’BAP–F3 HabCode_B H6u ‘D’inHabCodeandBAP_type2==‘H6u’BAP–F3 HabCode_B H6u HabCode==‘H1u’andBAP_type2==‘H6u’BAP–F3 HabCode_B H3/H5 ‘B’inHabCodeandBAP_type2==‘H3/H5’BAP–F3 HabCode_B H3/H5 ‘D’inHabCodeandBAP_type2==‘H3/H5’BAP–F3 HabCode_B H3/H5 HabCode==‘H1u’andBAP_type2==‘H3/H5’BAP–F3 HabCode_B H11 HabCode==‘B4/J11’andBAP_type2==‘H11’

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BAP–F3 HabCode_B H2u HabCode==‘H1u’andBAP_type2==‘H2u’BAP–F3 HabCode_B H2u ‘B’inHabCodeandBAP_type2==‘H2u’BAP–F3 HabCode_B H2u ‘D’inHabCodeandBAP_type2==‘H2u’BAP–F3 HabCode_B H8 HabCode==‘H1u’andBAP_type2==‘H8’BAP–F3 HabCode_B H8 ‘B’inHabCodeandBAP_type2==‘H8’BAP–F3 HabCode_B H8 ‘D’inHabCodeandBAP_type2==‘H8’BAP–F3 HabCode_B I13 ‘B’inHabCodeandBAP_type2==‘I13’BAP–F3 HabCode_B I13 ‘D’inHabCodeandBAP_type2==‘I13’BAP–F3 HabCode_B J13 ‘B’inHabCodeandBAP_type2==‘J13’BAP–F3 HabCode_B J13 ‘D’inHabCodeandBAP_type2==‘J13’OpenSpace HabCode_B J11t HabCode_B==‘B4/J11’andGI==1OpenSpace HabCode_B J12 HabCode_B==‘B4/J11’andGI==2and(BAP_p<%BAP_max%orBAP_pisNone)#%BAP_max%=0.4#OpenSpace HabCode_B J12 HabCode_B==‘B4/J11’andGI==3and(BAP_p<%BAP_max%orBAP_pisNone)#%BAP_max%=0.4#OpenSpace HabCode_B H1u HabCode_B==‘B4/J11’andGI==4#%BAP_max%=0.4#OpenSpace HabCode_B J12 HabCode_B==‘B4/J11’andGI==6and(BAP_p<%BAP_max%orBAP_pisNone)#%BAP_max%=0.4#OpenSpace HabCode_B J12 HabCode_B==‘B4/J11’andGI==7and(BAP_p<%BAP_max%orBAP_pisNone)#%BAP_max%=0.4#OpenSpace HabCode_B J12 HabCode_B==‘B4/J11’andGI==8and(BAP_p<%BAP_max%orBAP_pisNone)#%BAP_max%=0.4#LCAandLandscape HabCode_B J11t HabCode_B==’B4/J11’andLCA_code==1#%BAP_max%=0.4#LCAandLandscape HabCode_B J12 HabCode_B==’B4/J11’andLCA_code==2and(BAP_p<%BAP_max%orBAP_pisnone)#%BAP_max%=

0.4#LCAandLandscape HabCode_B J12 HabCode_B ==’B4/J11’ and LCA_code == 3 and Area_m > 5000 (BAP_p <%BAP_max% or BAP_p is none)

#%BAP_max%=0.4#LCAandLandscape HabCode_B J12 HabCode_B==’B4/J11’andLCA_code==4and(BAP_p<%BAP_max%orBAP_pisnone)#%BAP_max%=

0.4#LCAandLandscape HabCode_B B4 HabCode_B==’B4/J11’andLCA_code==5and(BAP_p<%BAP_max%orBAP_pisnone)#%BAP_max%=

0.4#LCAandLandscape HabCode_B J12 HabCode_B==’B4/J11’andLCA_code==6and(BAP_p<%BAP_max%orBAP_pisnone)#%BAP_max%=

0.4#LCAandLandscape HabCode_B B11m HabCode_B==’Bu1/Bu2’andLCA_code==7and(BAP_p<%BAP_max%orBAP_pisnone)#%BAP_max%=

0.4#LCAandLandscape HabCode_B B11m HabCode_B==’Bu’andLCA_code==7and(BAP_p<%BAP_max%orBAP_pisnone)#%BAP_max%=0.4#LCAandLandscape HabCode_B Bu HabCode_B==’B4/J11’andLCA_code==7and(BAP_p<%BAP_max%orBAP_pisnone)#%BAP_max%=



0.4#LCAandLandscape HabCode_B B4 HabCode_B==’B4/J11’andLCA_code==10and(BAP_p<%BAP_max%orBAP_pisnone)#%BAP_max%=


0.4#LCAandLandscape HabCode_B Bu HabCode_B==’B4/J11’andLCA_code==14and(BAP_p<%BAP_max%orBAP_pisnone)#%BAP_max%=

0.4#

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LINEAR HabCode_B Linear HabCode_B=='B4/J11'andArea_m>=15000andShape_index>50LINEAR HabCode_B Linear HabCode_B=='B4/J11'andArea_m>=10000andArea_m<15000andShape_index>40LINEAR HabCode_B Linear HabCode_B=='B4/J11'andArea_m>=7500andArea_m<10000andShape_index>30LINEAR HabCode_B Linear HabCode_B=='B4/J11'andArea_m>=5000andArea_m<7500andShape_index>25LINEAR HabCode_B Linear HabCode_B=='B4/J11'andArea_m>=1000andArea_m<5000andShape_index>20LINEAR HabCode_B Linear HabCode_B=='B4/J11'andArea_m>=500andArea_m<1000andShape_index>15LINEAR‐2 HabCode_B Linear ‘D’or‘Bu’inHabCode_BandShape_index>40andArea_m<=100,000Andnot‘A’inHabCode_Bandnot‘F’

inHabCode_BLINEAR‐2 HabCode_B Linear D’or‘Bu’inHabCode_BandShape_index>30andArea_m<=30,000Andnot‘A’inHabCode_Bandnot‘F’in

HabCode_BLINEAR‐2 HabCode_B Linear D’or‘Bu’inHabCode_BandShape_index>20andArea_m<=15,000Andnot‘A’inHabCode_Bandnot‘F’

inHabCode_BLINEAR‐2 HabCode_B Linear D’or‘Bu’inHabCode_BandShape_index>15andArea_m<=5,000Andnot‘A’inHabCode_Bandnot‘F’in

HabCode_BLCM_type LCM_type B11 LCM_AG_p>%LCM_min%#%LCM_min%=0.6#LCM_type LCM_type Bu1/Bu2 LCM_RG_p>%LCM_min%#%LCM_min%=0.6#LCM_type LCM_type Bu1_Bu2 LCM_NG_p>%LCM_min%#%LCM_min%=0.6#LCM_type LCM_type Bu1_Bu2 LCM_CG_p>%LCM_min%#%LCM_min%=0.6#LCM_type LCM_type B4 LCM_IG_p>%LCM_min%#%LCM_min%=0.6#LCM_type LCM_type B5/E3/F LCM_FMS_p>%LCM_min%#%LCM_min%=0.6#LCM_type LCM_type Du1 LCM_H_p>%LCM_min%#%LCM_min%=0.6#LCM_type LCM_type D5 LCM_HG_p>%LCM_min%#%LCM_min%=0.6#LCM_type LCM_type E1u LCM_BG_p>%LCM_min%#%LCM_min%=0.6#LCM_type LCM_type H1u LCM_LS_p>%LCM_min%#%LCM_min%=0.6#LCM_type LCM_type H13 LCM_LR_p>%LCM_min%#%LCM_min%=0.6#LCM_type LCM_type H26 LCM_ST_p>%LCM_min%#%LCM_min%=0.6#LCM_type LCM_type J11 LCM_J_p>%LCM_min%#%LCM_min%=0.6#Habitat+LCM HabCode_B B11 HabCode_B==‘Bu’andLCM_type==‘B11’Habitat+LCM HabCode_B B11 HabCode_B==‘B4/J11’andLCM_type==‘B11’Habitat+LCM HabCode_B B11 HabCode_B==‘D5’andLCM_type==‘B11’Habitat+LCM HabCode_B B11 HabCode_B==‘D/E’andLCM_type==‘B11’Habitat+LCM HabCode_B B11 HabCode_B==‘Bu1’andLCM_type==‘B11’Habitat+LCM HabCode_B D5h HabCode_B==‘Bu’andLCM_type==‘D5’Habitat+LCM HabCode_B D5h HabCode_B==‘B4/J11’andLCM_type==‘D5’andArea_m>20000Habitat+LCM HabCode_B D5h HabCode_B==‘Bu1’andLCM_type==‘D5’Habitat+LCM HabCode_B D5h HabCode_B==‘D/E’andLCM_type==‘D5’Habitat+LCM HabCode_B D5h HabCode_B==‘Bu1’andLCM_type==‘D5’Habitat+LCM HabCode_B D5h HabCode_B==‘D5’andLCM_type==‘D5’Habitat+LCM HabCode_B D5h HabCode_B==‘Unclassified’andLCM_type==‘D5’andArea_m>2500Habitat+LCM HabCode_B D5h_A2 HabCode_B==‘D5_Bu_Au’andLCM_type==‘D5’Habitat+LCM HabCode_B D5h_A2 HabCode_B==‘D5_Au’andLCM_type==‘D5’Habitat+LCM HabCode_B D5h HabCode_B==‘D5_B5/E3/F/H2’andLCM_type==‘D5’Habitat+LCM HabCode_B B4 HabCode_B==‘B4/J11’andLCM_type==‘B4’ANDShape<15

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Habitat+LCM HabCode_B B4 HabCode_B==‘Bu’andLCM_type==‘B4’andshp<15Habitat+LCM HabCode_B B4 HabCode_B==‘Unclassified’andLCM_type==‘B4’andArea_m>20000Habitat+LCM HabCode_B B5/E3/F HabCode_B==‘Bu’andLCM_type==‘B5/E3/F’andArea_m>5000Habitat+LCM HabCode_B B5/E3/F HabCode_B==‘Bu1’andLCM_type==‘B5/E3/F’andArea_m>5000Habitat+LCM HabCode_B B5/E3/F HabCode_B==‘B5/E3/F’andLCM_type==‘B5/E3/F’andArea_m>5000Habitat+LCM HabCode_B Bu1/Bu2 HabCode_B==‘B4/J11’andLCM_type==‘Bu1/Bu2’andArea_m>5000Habitat+LCM HabCode_B Bu1/Bu2 HabCode_B==‘D/E’andLCM_type==‘Bu1/Bu2’Habitat+LCM HabCode_B Bu1/Bu2 HabCode_B==‘Bu1’andLCM_type==‘Bu1/Bu2’Habitat+LCM HabCode_B Bu1/Bu2 HabCode_B==‘Bu’andLCM_type==‘Bu1/Bu2’Habitat+LCM HabCode_B Bu1/Bu2 HabCode_B==‘D5’andLCM_type==‘Bu1/Bu2’Habitat+LCM HabCode_B D1u HabCode_B==‘Bu’andLCM_type==‘D1u’Habitat+LCM HabCode_B D1u HabCode_B==‘Bu1’andLCM_type==‘D1u’Habitat+LCM HabCode_B D1u HabCode_B==‘D/E’andLCM_type==‘D1u’Habitat+LCM HabCode_B D1u HabCode_B==‘D5’andLCM_type==‘D1u’Habitat+LCM HabCode_B D1u HabCode_B==‘D5_B5/E3/F/H2’andLCM_type==‘D1u’Habitat+LCM HabCode_B D1u HabCode_B==‘B4/J11’andLCM_type==‘D1u’Habitat+LCM HabCode_B E1u HabCode_B==‘Bu’andLCM_type==‘E1u’Habitat+LCM HabCode_B E1u HabCode_B==‘Bu1’andLCM_type==‘E1u’Habitat+LCM HabCode_B E1u HabCode_B==‘D/E’andLCM_type==‘E1u’Habitat+LCM HabCode_B E1u HabCode_B==‘D5’andLCM_type==‘E1u’Habitat+LCM HabCode_B E1u HabCode_B==‘B4/J11’andLCM_type==‘E1u’andArea_m>5000Habitat+LCM HabCode_B H1u HabCode_B==‘B4/J11’andLCM_type==‘H1u’andArea_m>5000Habitat+LCM HabCode_B H1u HabCode_B==‘G26’andLCM_type==‘H1u’andArea_m>5000Habitat+LCM HabCode_B H1u HabCode_B==‘Bu’andLCM_type==‘H1u’andArea_m>5000Habitat+LCM HabCode_B H1u HabCode_B==‘Bu1’andLCM_type==‘H1u’andArea_m>5000Habitat+LCM HabCode_B H13 HabCode_B==‘H1u’andLCM_type==‘H13’andArea_m>2500Habitat+LCM HabCode_B H13 HabCode_B==‘G26’andLCM_type==‘H13’andArea_m>2500Habitat+LCM HabCode_B H13 HabCode_B==‘B4/J11’andLCM_type==‘H13’andArea_m>2500Habitat+LCM HabCode_B H26 HabCode_B==‘B5/E3/F/H2’andLCM_type==‘H26’Habitat+LCM HabCode_B H26 HabCode_B==‘H1u’andLCM_type==‘H26’Habitat+LCM HabCode_B H26 HabCode_B==‘Bu’andLCM_type==‘H26’Habitat+LCM HabCode_B H26 HabCode_B==‘Bu1’andLCM_type==‘H26’Habitat+LCM HabCode_B H26 HabCode_B==‘B4/J11’andLCM_type==‘H26’andArea_m>15000Habitat+LCM HabCode_B J11 HabCode_B==‘B4/J11’andLCM_type==‘J11’andArea_m>15000andShape_index<15Slopes HabCode_B Bu1 HabCode_B=='Bu1/Bu2'andSlope_mean>%Unimprovedslopes%#%Unimprovedslopes%=18#Slopes HabCode_B Bu1 HabCode_B=='Bu'andSlope_mean>%Unimprovedslopes%#%Unimprovedslopes%=18#Slopes HabCode_B Bu1 HabCode_B=='Bu2'andSlope_mean>%Unimprovedslopes%#%Unimprovedslopes%=18#Slopes HabCode_B Bu HabCode_B=='B4'andSlope_mean>%Semi‐improvedslopes%andSlope_mean<=%Unimprovedslopes%

#%Unimprovedslopes%=18%Semi‐improvedslopes%=11#Slopes HabCode_B Bu HabCode_B=='B4f'andSlope_mean>%Semi‐improvedslopes%andSlope_mean<=%Unimprovedslopes%

#%Unimprovedslopes%=18%Semi‐improvedslopes%=11#Slopes HabCode_B Bu1 HabCode_B == 'B4' and Slope_mean >%Unimproved slopes% #%Unimproved slopes% = 18 %Semi‐

improvedslopes%=11#

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Slopes HabCode_B Bu1 HabCode_B=='B4/J11andSlope_mean>%Unimprovedslopes%#%Unimprovedslopes%=18%Semi‐improvedslopes%=11#

Slopes HabCode_B Bu HabCode_B == 'B4/J11' and Slope_mean > %Semi‐improved slopes% and Slope_mean <= %Unimprovedslopes%#%Unimprovedslopes%=18%Semi‐improvedslopes%=11#

Slopes HabCode_B Bu1 HabCode_B == 'J11' and Slope_mean >%Unimproved slopes% #%Unimproved slopes% = 18 %Semi‐improvedslopes%=11#

Slopes HabCode_B Bu HabCode_B=='J11'andSlope_mean>%Semi‐improvedslopes%andSlope_mean<=%Unimprovedslopes%#%Unimprovedslopes%=18%Semi‐improvedslopes%=11#

Slopes HabCode_B Du HabCode_B=='D5_B5/E3/F/H2'andSlope_mean>%Dryslopes%#%Unimprovedslopes%=18%Semi‐improvedslopes%=11#

Slopes HabCode_B Du HabCode_B == 'E2/E3/F1' and Slope_mean > %Dry slopes% #%Unimproved slopes% = 18 %Semi‐improvedslopes%=11#

Urban HabCode_B J13Urb HabCode_B=='J13'andUrb>%Urbanoverlap%#%Urbanoverlap%=0.8#Urban HabCode_B J55Urb HabCode_B=='J55'andUrb>%Urbanoverlap%#%Urbanoverlap%=0.8#Urban HabCode_B B4/J11Urb HabCode_B=='B4/J11'andUrb>%Urbanoverlap%#%Urbanoverlap%=0.8#Urban HabCode_B J12 HabCode_B=='B4/J11'andUrb>%Urbanoverlap%#%Urbanoverlap%=0.8#Urban HabCode_B J12 HabCode_B=='B4'andUrb>%Urbanoverlap%#%Urbanoverlap%=0.8#Urban HabCode_B J56Urb HabCode_B=='J56'andUrb>%Urbanoverlap%#%Urbanoverlap%=0.8#Areaandshape HabCode_B B4 HabCode_B==‘B4/J11’andArea_m<%Arable_min%#%Arable_min%=5000#Areaandshape HabCode_B J11 HabCode_B==‘B4/J11’andArea_m>%Improved_max%#%Improved_max%=25000#HabNat HabNat !HabCode_B! =HabCode_BAWI HabNat A11_AW HabCode_B=='A11'orHabCode_B=='A11/A2'orHabCode_B=='A31'orHabCode_B=='A33'orHabCode_B

=='A13'orHabCode_B=='A13/A2'andAWIa>%AWIoverlap%#%AWI_overlap%=0.8#AWI HabNat A12_AW HabCode_B=='A12'orHabCode_B=='A12/A2'andAWIa>%AWIoverlap%#%AWI_overlap%=0.8#Montane HabNat Montane make=='Natural'and'G'notinHabCode_BandElev_mean>%Montane%#%Montane%=600#GItypeFINAL GI_Type_Final GI_Beach (Make=='Natural')and(GI==4)GItypeFINAL GI_Type_Final GI_Allotments (DescGroup!='RoadorTrack'andDescGroup!='Roadside'andGI==1and(Make=='Natural'orMake==

'Multiple')and(BAP_p<0.7orBAP_pisNone))GItypeFINAL GI_Type_Final GI_Amenity (DescGroup=='Path'andGI==2)GItypeFINAL GI_Type_Final GI_Amenity (DescGroup!='Rail'andMake=='Natural'andGI==2)GItypeFINAL GI_Type_Final GI_Cemeteries (DescGroup=='Path'andGI==3):GItypeFINAL GI_Type_Final GI_Cemeteries (GI==3andMake=='Natural'):GItypeFINAL GI_Type_Final GI_Cemeteries (GI==3andMake=='Multiple'andShape_area>350):GItypeFINAL GI_Type_Final GI_Mixed (DescGroup=='Path'andGI==9):GItypeFINAL GI_Type_Final GI_Mixed (GI==9andMake=='Multiple'andShape_area>350GItypeFINAL GI_Type_Final GI_Mixed (GI==9andMake=='Natural')GItypeFINAL GI_Type_Final GI_Semi_natural (DescGroup=='Path'andGI==5):GItypeFINAL GI_Type_Final GI_Semi_natural (GI==5andMake=='Multiple'andShape_area>350):GItypeFINAL GI_Type_Final GI_Semi_natural (GI==5andMake=='Natural'):GItypeFINAL GI_Type_Final GI_Park (DescGroup=='Path'andGI==6):GItypeFINAL GI_Type_Final GI_Park (GI==6andMake=='Multiple'):GItypeFINAL GI_Type_Final GI_Park (GI==6andMake=='Natural')GItypeFINAL GI_Type_Final GI_Play (GI==7andDescGroup!='Building'):

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Fieldupdated

Attributeassigned


GItypeFINAL GI_Type_Final GI_Sports (GI==8andDescGroup!='Building')GItypeFINAL GI_Type_Final GI_Woods (DescGroup=='Path'andGI==10):GItypeFINAL GI_Type_Final GI_Woods (GI==10andMake=='Multiple'andShape_area>350):GItypeFINAL GI_Type_Final GI_Woods (GI==10andMake=='Natural'):Populateaccessible FCScotwoodtypes HabCode_B A112 HabCode_B=='A11'andFC_SN_woods==8888andArea_m>5000FCScotwoodtypes HabCode_B A31 HabCode_B=='A11'andFC_SN_woods==8888andArea_m<=5000FCScotwoodtypes HabCode_B A111 HabCode_B=='A11'andFC_SN_woods==1FCScotwoodtypes HabCode_B A112/A2 HabCode_B=='A11/A2'andFC_SN_woods==8888andArea_m>5000FCScotwoodtypes HabCode_B A31/A2 HabCode_B=='A11/A2'andFC_SN_woods==8888andArea_m<=5000FCScotwoodtypes HabCode_B A111/A2 HabCode_B=='A11/A2'andFC_SN_woods==1FCScotwoodtypes HabCode_B A32 HabCode_B=='A12'andFC_SN_woods==8888andArea_m<=5000FCScotwoodtypes HabCode_B A122 HabCode_B=='A12'andFC_SN_woods==8888andArea_m>5000FCScotwoodtypes HabCode_B A121 HabCode_B=='A12'andFC_SN_woods==1FCScotwoodtypes HabCode_B A121/A2 HabCode_B=='A12/A2'andFC_SN_woods==1FCScotwoodtypes HabCode_B A122/A2 HabCode_B=='A12/A2'andFC_SN_woods==8888andArea_m>5000FCScotwoodtypes HabCode_B A32/A2 HabCode_B=='A12/A2'andFC_SN_woods==8888andArea_m<=5000FCScotwoodtypes HabCode_B A132 HabCode_B=='A13'andFC_SN_woods==8888andArea_m>5000FCScotwoodtypes HabCode_B A33 HabCode_B=='A13'andFC_SN_woods==8888andArea_m<=5000FCScotwoodtypes HabCode_B A131 HabCode_B=='A13'andFC_SN_woods==1FCScotwoodtypes HabCode_B A131/A2 HabCode_B=='A13/A2'andFC_SN_woods==1FCScotwoodtypes HabCode_B A132/A2 HabCode_B=='A13/A2'andFC_SN_woods==8888andArea_m>5000FCScotwoodtypes HabCode_B A33/A2 HabCode_B=='A13/A2'andFC_SN_woods==8888andArea_m<=5000FCScotwoodtypes HabCode_B Bu_A112/A2 HabCode_B=='Bu_A1/A2'andFC_SN_woods==8888andArea_m>5000FCScotwoodtypes HabCode_B Bu_A2/A3 HabCode_B=='Bu_A1/A2'andFC_SN_woods==8888andArea_m<=5000FCScotwoodtypes HabCode_B Bu_A111/A2 HabCode_B=='Bu_A1/A2'andFC_SN_woods==1andArea_m>5000FCScotwoodtypes HabCode_B Bu_A2/A3 HabCode_B=='Bu_A1/A2'andFC_SN_woods==1andArea_m<=5000WoodsandTrees HabCode_B A31 HabCode_B=='A11'andArea_m<=5000WoodsandTrees HabCode_B A31/A2 HabCode_B=='A11/A2'andArea_m<=5000WoodsandTrees HabCode_B A32 HabCode_B=='A12'andArea_m<=5000WoodsandTrees HabCode_B A32/A2 HabCode_B=='A12/A2'andArea_m<=5000WoodsandTrees HabCode_B A33 HabCode_B=='A13'andArea_m<=5000WoodsandTrees HabCode_B A33/A2 HabCode_B=='A13/A2'andArea_m<=5000WoodsandTrees HabCode_B Bu_A31 HabCode_B=='Bu_A11'andArea_m<=5000WoodsandTrees HabCode_B Bu_A32 HabCode_B=='Bu_A12'andArea_m<=5000WoodsandTrees HabCode_B Bu_A2/A3 HabCode_B=='Bu_A1/A2'andArea_m<=5000Heathpatches HabCode_B Bu HabCode_B==‘D5’andArea_m<=%Heath_grass_min%#Heath_grass_min=5,000#Heathpatches HabCode_B Bu HabCode_B==‘D5h’andArea_m<=%Heath_grass_min%#Heath_grass_min=5,000#Heathpatches HabCode_B Bu HabCode_B==‘D/E’andArea_m<=%Heath_min%#Heath_min=10,000#Addseparatefields DataSources concat(!DataOS!,!Data03!,!Data04!,!Data05!,!Data06!,!Data07!,!Data08!,!Data09!,!Data10!,!Data11!)

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Metadata: BaseMap_FINAL selected attribute fields (because some models are optional not all attribute fields may have data content in all

runs of the Toolkit)

Fieldname

Contents+SourcedataorRuleBase

OBJECTID OSMasterMap–uniqueobjectIDShape OSMasterMap–shapeToid OSMasterMap–uniqueIDcreatedbyOrdnanceSurveyDescGroup OSMasterMap–TextdescriptorofpolygoncontentDescTerm OSMasterMap–TextdescriptorofpolygoncontentMake OSMasterMap–Textdescriptorofpolygoncontent,illustratingManmadeornaturalsurfacesArea_m Calculation=Shape_areaLength_m Calculation=Shape_LengthSlv_shp (3.1415926535897932384626433832795*(([Shape_Length]/(2*3.1415926535897932384626433832795))^2))/[Shape_Area]Constant1 Constant=1Constant0 Constant=0NoData Constant=‐9999Shape_index (3.1415926535897932384626433832795*(([Shape_Length]/(2*3.1415926535897932384626433832795))^2))/[Shape_Area]SameasMerseyForestShape_index2 [Length_m]/(2*Sqr(3.14159265359*[Area_m])).GenerallandscapeecologyindexPARA Perimeter/area.PARAequalstheratioofthepatchperimeter(m)toarea(m2)GI_type ThemainGI/OpenSpacetypethatoverlapswithaMMpolygon.Textnamedescription.GI Coderepresentingmain/dominantGI/OpenSpaceTypeOST_var Thenumber(variety)ofdifferentGI/OpenSpaceTypesthatoverlapapolygonLCA_var ThevarietyofindividualLCAtypesthatoverlappedwiththepolygon.(Highervaluesmaymeanmoreuncertaintyastothedominanttype)LCA_type ThemainLCAtypethatoverlapsthemajorityoftheOSMMpolygons(text)LCA_code ThemainLCAtypethatoverlapsthemajorityoftheOSMMpolygons(numericcode)HabCode AmendedPhase1habitatcodeindicatingthepredicted/classifiedhabitattypeofeachpolygon.Thisisanintermediatestage,usedforlaterre‐classification.FnlHCode ListiftheHabCodeisafinalclassificationofsubjecttoreclassificationoramendmentbyadditionalinfosources(usedinupdatemodels).Elev_mean Themeanelevationvalueperpolygon.(CalculatedfromthesourceDTM)(m)Elev_range Theelevationrangevalueperpolygon.(CalculatedfromthesourceDTM)(m)Slope_mean Themeanslopevalueperpolygon.(CalculatedfromthesourceDTM)(m)Slope_max Themaximumslopevalueperpolygon.(CalculatedfromthesourceDTM)(m)Slope_min Theminimumslopevalueperpolygon.(CalculatedfromthesourceDTM)(m)Slope_range Thesloperangevalueperpolygon.(CalculatedfromthesourceDTM)(m)Asp_maj Themajor(mostcommon)aspectvalueperpolygon.NOTCURENLTYIMPLEMENTEDINTHISVERSIONAsp_var Thevarietyofaspectvaluesperpolygon.NOTCURENLTYIMPLEMENTEDINTHISVERSIONUrb Value=1wherethepolygoniswithinanurbanareaAWIa Proportionofthepolygoncoveredbysemi‐naturalAncientWoodland.HabNat AcopyoftheclosedPhase1habitattypecode,describingeachcoded.AmendedforusewithclassificationofpatchnaturalnessT_ID UniqueincrementingID.BecauseallduplicateshavebeenremovedbasedonTOID,thisisakintousingTOID.UsedbysomemodelsthatneednumericID.%variousBAP%_p ProportionofthepolygoncoveredbythisBAPhabitattype.SeedatapreparationtableforinterpretationofBAPtypecodes.

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Fieldname

Contents+SourcedataorRuleBase

LWS_p ProportionofthepolygoncoveredbyLWS(LocalWildlifeSite)(Seeglossary)BAP_p ProportionofthepolygoncoveredbyanytypeofBAPhabitat.CalculatedfromSUMofzonalstatsoverlap/polygonarea.%variousLCM%_p ProportionofthepolygoncoveredthisLCM2007landcovertype.SeedatapreparationtableforinterpretationofLCMtypecodes.%variousLCM% Thearea(m)withinthepolygoncoveredbythisLCMtype.SeedatapreparationnotesforinterpretationofLCMtypecodes.DataOS IndicatesthedataisclassifiedbyOSMasterMapdataData%various% Indicates thatmodel%number% has been run and the dataset has been classified by incorporating data . Refers to the whole dataset not individual

polygons.DataSources ListallthedatasetcodesthathavecontributedtotheclassificationofthepolygonsinthisdatasetAccessGI Classifiesifeachpolygonisconsideredtobepubliclyaccessible(1)ornot(2)orunknown(Null/None)FC_SN_woods Classifiesthepolygonasnativesemi‐naturalwoodland(1)ornot(8888)BAP_sum ProportionofthepolygoncoveredbyanytypeofBAPhabitat.CalculatedfromthesumofeachseparateBAPproportionfield.BAP_type2 Textcodeclassifyingthemain/dominantBAPtypeoccurringwithinthepolygon.SeeBAPdatapreparationforcodedescriptions.HabCode_B AmendedPhase1habitatcodeindicatingthepredicted/classifiedhabitattypeofeachpolygon.Thisistheprimaryfieldusedtoclassifythehabitatofeach

polygonLCM_type Textcodeclassifyingthemain/dominantLCMtypeoccurringwithinthepolygon.SeeLCMdatapreparationforcodedescriptions.GI_Type_Final Textcodeclassificationofthe“GI_type”presentineachpolygon.ThisusescodesinfieldGI_TypeandcrossvalidatesthemagainsttheunderlyingOSdatato

verifyareasofGI/OpenSpace.Accessible 1=accessiblePh1code ReferencePhase1habitatcode–thisisusedtolinktofieldHabCode_BHabNmPLUS Mostdetailedtextbasedclassificationofthehabitattype(maxupto206habitattypes)HabBroad Broadtextbaseddescriptionofhabitattype,amalgamatedcategories(maxupto39types)HabClass Classtextbaseddescriptionofhabitattype,furtheramalgamatedintocategories(maxupto15types)HabCertainty Textbasedclassificationofthebroadcategoryofcertaintyofintheclassificationaccuracyofeachhabitattype.(Basedonlikelydatasourcesusedtoclassify

eachcategoryofhabitat)HabCertScr NumericalversionofHabCertaintyequicLCM2007 ThenearestequivalentLCM2007categoryofeachmappedpolygon.TotCarb Estimatedcarboncontentofvegetationforeachhabitattype.AirPurScore Estimatedscoreofthecapacityofeachhabitattypetoabsorb/mitigateairpollution.Noise Estimatedscoreofthecapacityofeachhabitattypetoabsorb/mitigatenoisepollution.Rough Classified“roughness”scoresforeachhabitattype.HabNmbr Numbergiventogroupsofhabitats,foruseinmodelcalculations.Erosion Classifiederosionsusceptibilityofeachhabitattype.equicCLC2000 ThenearestequivalentCLC2000categoryofeachmappedpolygon.CostWood Estimateddispersalcostsforhypothetical“woodland”specieswithineachhabitattype–usedwithinleastcostmodellingforecologicalnetworks.Costheath Estimateddispersalcostsforhypothetical“heathland”specieswithineachhabitattype–usedwithinleastmodellingforecologicalnetworksCostMire Estimateddispersalcostsforhypothetical“mire”specieswithineachhabitattype–usedwithinleastmodellingforecologicalnetworksCostGrass Estimateddispersalcostsforhypothetical“grassland”specieswithineachhabitattype–usedwithinleastmodellingforecologicalnetworksCostPond Estimateddispersalcostsforhypothetical“woodland”specieswithineachhabitattype–usedwithinleastmodellingforecologicalnetworksPh1Colour Phase1Habitatsurvey(JNCC)equivalentcode(detailed)–usedtolinktosavedlayerfileswithinArcMapPh1ColBestFit Phase1Habitatsurvey(JNCC)equivalentcode(nearestbroadcategory)–usedtolinktosavedlayerfileswithinArcMap

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Metadata: Pop_socio_points attribute field descriptions (selected)

Note: the order of fields within the GIS data layer differs slightly from that presented below

Field name Content (Range) Source geography TOID OS polygon identifier OSMM DescGroup OS text classification OSMM DescTerm OS text classification OSMM OAC ONS polygon reference code OA/DZ Persons Number of people per OA / DZ OA/DZ Households Number of households per OA / DZ OA/DZ House_pop Persons / houses = house pop OA/DZ All_people_x Repeat of total population, for checks OA/DZ Under10 Total population count up to and including 9 OA/DZ Un10prop Proportion of total population count up to and including 9 OA/DZ Under15 Total population count up to and including 14 OA/DZ Un15prop Proportion of total population count up to and including 14 OA/DZ Under18 Total population count up to and including 17 OA/DZ Un18prop Proportion of total population count up to and including 17 OA/DZ Under20 Total population count up to and including 19 OA/DZ Un20prop Proportion of total population count up to and including 19 OA/DZ 065plus Total population count over 65 OA/DZ Risk_group Proportion of total population count over 65 OA/DZ Ethn_SnD_OA Simpsons Index (1‐ Simpsons) (range: 0 to 1) OA/DZ Ethn_D2_OA Simpsons (range: 0 to 1) OA/DZ Ethn_InvD_OA Inverse Simpsons Index (1 / Simpsons Index) (range 1 to max 5) OA/DZ All_16_74 Total number of residents 16 to 74 OA/DZ NSEC1 Number of people per ‐ social group OA/DZ NSEC2 Number of people per ‐ social group OA/DZ NSEC3 Number of people per ‐ social group OA/DZ NSEC4 Number of people per ‐ social group OA/DZ NSEC5 Number of people per ‐ social group OA/DZ NSEC6 Number of people per ‐ social group OA/DZ NSEC7 Number of people per ‐ social group OA/DZ NSEC8 Number of people per ‐ social group OA/DZ NS_SnD_OA Simpsons Index (1‐ Simpsons) (range 0 to 1) OA/DZ NS_D2_OA Simpsons (range 0 to 1) OA/DZ NS_InvD_OA Inverse Simpsons Index (1 / Simpsons Index) (range 1 to max 9) OA/DZ Christian Number of people per ‐ religion OA/DZ Buddhist Number of people per ‐ religion OA/DZ Hindu Number of people per ‐ religion OA/DZ Jewish Number of people per ‐ religion OA/DZ Muslim Number of people per ‐ religion OA/DZ Sikh Number of people per ‐ religion OA/DZ Other_religion Number of people per ‐ religion OA/DZ No_religion Number of people per ‐ religion OA/DZ Rel_SnD_OA Simpsons index (1‐Simpsons) (range 0 to 1) OA/DZ Rel_D2_OA Simpsons (range: 0 to 1) OA/DZ Rel_InvD_OA Inverse Simpsons index (1 / Simpsons Index) (range 1 to max 9) OA/DZ Total_Pop Total population (per LSOA) LSOA Work_Age Total working age population (per LSOA) LSOA IMDScor IMD score (per LSOA) Higher score = more deprived LSOA IMDRank IMD rank (per LSOA) Lowe number / higher rank = more deprived LSOA IncomeScor IMD component score LSOA IncomePeople IMD component score LSOA IncomeRank IMD component rank LSOA EmployScor IMD component score LSOA EmployPeople IMD component score LSOA EmployRank IMD component rank LSOA

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Field name Content (Range) Source geography HealthScor IMD component score LSOA HealthRank IMD component rank LSOA EducatScor IMD component score LSOA EducatRank IMD component rank LSOA HouseScor IMD component score LSOA HouseRank IMD component rank LSOA AccesScor IMD component score LSOA AccesRank IMD component rank LSOA CrimeScor IMD component score LSOA CrimeRank IMD component rank LSOA

Documents

EcoServ-GIS v3.3 Technical Report: “BaseMap (Habitat Map)”...information on habitat type / land cover. Land cover and habitat mapping data can be used to add additional information