This lab was about preparing MEDS for future use in an
analysis for Homeland Security. MEDS,
Minimum Essential Data Sets, were developed by a joint effort of National
Geospatial-Intelligence Agency, USGS and the Federal Geographic Data Committee
through the Homeland Security Infrastructure Program (HSIP). This data is critical for successful homeland
security operation. There are specific
data requirements depending on whether an area is classed as an Urban Area or
Large Area. With over 3,300 counties and
85,000 municipalities in the United States, obtaining relevant, quality data is
quite a challenge. Levels of of
geospatial data collection and management vary among these areas and is
constantly evolving. The MEDS criteria
help to create essential requirements and organization for the vast amounts of
data collected throughout the United States.
The data sets stipulated by the Department of Homeland Security (DHS)
are Boundaries, Hydrography, Elevation, Transportation, Land Cover,
OrthoImagery, Structures and Geographic Names.
MEDS data can be used to determine locations for surveillance cameras,
where to place road blocks in the event of elevated security needs, areas which
may be targets of terrorism, areas of potential mass gatherings requiring additional
security measures and numerous other uses.
This comprehensive geospatial database is critical for not only homeland
security, but also local communities in order to prevent, prepare, respond and
recover during catastrophic events. Here
is our example of a MEDS dataset for:
As I typically do, I outline my steps as I create my
map. This helps me to remember specific
steps for future use as well as create my data consistent and organized. If I have difficulty with results, my steps
are documented for troubleshooting purposes. (The outline formatting does not translate well into the blog, but I think the gist of it is understandable.)
I.
Module 6 Meds Prepare
A.
Preparing Meds Map
1.
Review
all data and complete metadata table
2.
Create
.mxd, name the data frame Boston MEDS Set display units to Meters and import
coord system from Boundaries
3.
Create
Group Layers for each of the themes; add layers for Boundaries and
Transportation and leave remaining layer groups empty.
B.
Manipulate Transportation Data
1.
Review
the BMSA_Roads_pm attribute table and study the CFCC codes
2.
Add
the crcc table from the Boston_Data GDB and open to view table
3.
BMSA_Roads_pm,
Join attributes from a table CFCC; keep all records create index if asked
C.
Export Roads by CFCC Classsifications
1.
Select
by Attributes
a)
cfcc.CFCC >= 'A41' AND cfcc.CFCC <= 'A45'
b)
data, export data same coord as feature dataset
c)
BMSA_Roads_Local_rh, add to map
d)
Turn off BMSA_Roads_pm
2.
Adjust
Symbology of Local Roads
a)
Categories/Unique Values/CFCC
b)
All symobls = transportation, A15 width 1
3.
Repeat
steps for Primary Roads and Secondary Roads
a)
cfcc.CFCC >= 'A11' AND cfcc.CFCC <=
'A25'Primary – A20 width 1.5 and cfcc.CFCC >= 'A29' AND cfcc.CFCC <=
'A38' Secondary A25 Width 1.25
4.
Show
features at Specified Scale Range
a)
In the general tab for the BMSA_Roads_Local_rh
set don’t show layer when zoomed out beyond 1:100,000
b)
Secondary roads 1:250,000, primary all scales
5.
Show
labels at specified Ranges
a)
On labels tab for BMSA_Roads_Local_rh, scale
range don’t show out beyond 1:24,000
b)
Label field should be Full_Street_Name, used
label style for North American Streets.
Set Character spacing to 6
c)
Repeat for Primary and Secondary making Primary
12 and Secondary 10, change color for each.
D.
Add Data to Hydrography Group Layer
1.
Add
NHDWaterboyd, NHDArea, and NHDPoint feature classes
E.
Edit Land Cover Symbology
1.
Extract
Land Cover Raster by BMSA Boundary Mask
a)
Add Land Cover raster to land cover group layer
b)
Spatial analyst tools/Extraction/Extract by Mask
(1)
Inupt is Landcover; input feature mas is
BMSA_Boundary_pm; ouput raster is BMSA_LC_rh (run in ArcCatalog with ArcMap
closed)
2.
Set
dataframe extents to fixed extent BMSA_Boundary_pm
3.
Change
symbology by adding a color map
a)
On symbology tab select coloormap/import
colormap/NLCD.clr from BostonData.gdb
b)
Edit labels per lab instructions
F.
Add Orthoimagery and Elevation Layers
1.
Add
BMSA_Ortho_pm to orthoimagery group and BMSA_DEM_pm to the elevation group
layer.
a)
Change the pm to rh in the layer name
G.
Add Geographic Names
1.
Modify
the schema ini file
a)
Format=Delimited(|)
2.
Create
a Geographic names feature class from xy table
a)
ArcCatalog MA_Features_20130404.txt, Create
Feature Class From XY Table
b)
Select Prim_Long_Dec from x drop down
c)
Select Prim_Lat_Dec from y drop down
d)
Set coord of input cords to GCS NAD 1983
(Geographic Coordinate System North America NAD
1983
e)
Save output as MA_GNIS_rh
3.
Create
new group layer Geographic Names in mxd
a)
Add MA_GNIS_rh
b)
Data management tools/projections and
transformation/feature/project
(1)
Input MA_GNIS_rh, ouput dataset or feature class
as MA_GNIS_SPCS_rh, output coord system NAD 1983 SP Mass Mainland FIPS 2001
4.
Select
by Attributes
a)
Select 6 counties that make up boston
metropolitan statistical area
b)
COUNTY_NAME = 'Bristol' OR COUNTY_NAME = 'Essex'
OR COUNTY_NAME = 'Middlesex' OR COUNTY_NAME = 'Norfolk' OR COUNTY_NAME =
'Plymouth' OR COUNTY_NAME = 'Suffolk'
c)
Clear selection and the use select by location
and use BMSA_Boundary_rh and lying
completely within.
d)
Export to BMsA_GNIS_rh using the same coord as
the data frame and add to map
e)
Move to Geographic Names layer group and change
symbology per lab instructions
f)
Label using feature name, arial 8 burnt umber
scale range 1:24,000 don’t zoom out beyond.
H.
MEDS data management
1.
Save
each layer group as a layer file
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