1| Page 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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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.
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
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.
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% / BaseMap01b_%RunCode%_%outDate%.
The “RunCode” is set to allow each run of the Models to be coded by a StudyArea and / or run specific code
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).
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.
2 Short, improvedgrasslandusedwhichmaybeused for recreationor to fill spacebetweenhousingandgreyinfrastructure(e.g.roadverges).Mayalsoincludesportsfacilities.
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.
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. Slope_mean, Slope_min, Slope_max and Slope_range are
then populated.
Made into a feature layer, Clear selection, to make sure no selections are present.
Delete field is used to delete all the original min, max, variety etc fields.
Field Data06 is calculated as "6" to indicate the data has been classified by this model.
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% / BaseMap06_Slopes. o copied to %Outputs% / BaseMap06_Slopes_%RunCode%_%outDate%.
The “RunCode” is set to allow each run of the Models to be coded by a StudyArea and / or run specific code
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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.
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.
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.
(At selected stages the in_memory files are deleted, to free up space).
Passes output to Sub‐model – Zonal_4_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.
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.
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).
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.
Made into a feature layer, Clear selection, to make sure no selections are present.
Delete field is used to delete all the original min, max, variety etc fields.
Field Data07 is calculated as "7" to indicate the data has been classified by this model.
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.
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o copied to %Outputs% / BaseMap07_Elevation. o copied to %Outputs% / BaseMap07_Elevation_%RunCode%_%outDate%.
The “RunCode” is set to allow each run of the Models to be coded by a StudyArea and / or run specific code
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.
Initial sub‐models run to delete any existing copies of results from previous runs of the 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.
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% / BaseMap08_SNW. o copied to %Outputs% / BaseMap08_SNW_%RunCode%_%outDate%.
The “RunCode” is set to allow each run of the Models to be coded by a StudyArea and / or run specific code
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.
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.
Field Data09 is calculated as "9" to indicate the data has been classified by this model.
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% / 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
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.
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.
Field Data10 is calculated as "10" to indicate the data has been classified by this model.
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% / BaseMap10_Urban. o copied to %Outputs% / BaseMap10_Urban_%RunCode%_%outDate%.
The “RunCode” is set to allow each run of the Models to be coded by a StudyArea and / or run specific code
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.
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% / BaseMap11_Habitats. o copied to %Outputs% / BaseMap11_Habitats_%RunCode%_%outDate%.
The “RunCode” is set to allow each run of the Models to be coded by a StudyArea and / or run specific code
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.
Finally the In Memory space is deleted.
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)
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)
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
#%Unimprovedslopes%=18%Semi‐improvedslopes%=11#Slopes HabCode_B Bu HabCode_B=='B4f'andSlope_mean>%Semi‐improvedslopes%andSlope_mean<=%Unimprovedslopes%
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 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#
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
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