Pre-processing

Some pre-processing outside of python was required to prepare some datasets. Those processing steps are outlined below.

Prepare parcels and right of way segments

Right of way segments and parcels are joined together to create the baseline geography for analysis via the function get_parcels_row() in ms4_funcs.py

Right of way segments must be prepared using an ArcPy notebook (can be run in ArcGIS Pro). Parcel data is generated directly from MassGIS via their pre-processed MBTA Communities layers. For non-MBTA communities, parcel data can be generated directly from MassGIS.

Right of way segments

Update parameters and run model in row_segmentation.ipynbCan be run in ArcGIS Pro or in an IDE with ArcPy environment.

Parcels

• For MBTA Municipalities – use detailed, pre-prepared parcel data from state. Should be unzipped already at ‘section3a_parcels_path’ in make_dataset.py

• For Non-MBTA communities – (manual preparation): download parcel data from state, join assessors data and land use descriptions (all part of download). Script currently reads those from a geodatabase layer ‘ms4_model_gdb’ in make_dataset.py

Nutrient Load estimation

Based on guidance from MassDEP, we developed a Model-Builder based ArcPy script pler_calc.py

Inputs

• 2016 Land Cover/Land Use (clipped and with geometry simplified using the Simplify Polygon tool in ArcGIS Pro [retain critical bends])

• SSURGO soils layer

Outputs

Adds a field called 'pler' to the Land Cover / Land Use layer, with the multiplier value for Phosphorus load estimate. Serves as an input for several functions within the model.

Prepare local drainage data

This was done manually for this iteration. MAPC plans to script this in the future.

Note that drainage system data came in many forms: in geodatabases or as zipped shapefiles. Layers themselves were sometimes merged together (ie, all “point” types in one layer with a field distinguishing what type of point it is) or in separate layers (ie, one layer for catch basins, one for outlets, etc). In some, drainage layers were sent with sewage system layers.

One single geodatabase was created for this project (drainage_network_gdb in make_dataset.py), with two different feature datasets: drainage_lines and drainage_pts distinguishing between two different types of data.

Points

1. Need one layer for all points that are stormwater “sinks” : catch basins and culvert inlets

2. Export to a gdb with other town’s point and line layers. Include town name and ‘pts’ in layer name

Lines

1. Need one layer for all pipes that are part of the stormwater system

2. Export to a gdb with other town’s point and line layers. Include town name and ‘lines' in layer name

Prepare community visibility layer

The community visibility layer is a point dataset containing points for high-traffic community sites (schools, libraries, town halls, long-term care facilities, recreation centers, farmers markets, places of worship, and MBTA stops). The layer has additional fields ('TYPE' and 'NAME') to distinguish the kind of site associated with the point, and the name of that site.

An R Script, ms4-comm-vis.R, was developed to create this layer.