Spatial Network Analysis Routine Functions

Built in aggregate functions for multiple runs of the network analysis tools

Routines Functions

ReachAggregate func

ReachAggregate(GraphSm:GraphSims, MeasureDf:pd.DataFrame, **kwargs)

returns GraphSims : GraphSims class object
GraphSims object

Runs a set of Reach functions available on the GraphSim functions. Reads the set of runs from a dataframe. See MeasureDf explanation for input sets. Outputs GraphSims with self.EntriesDf appended with results, with naming fill be

result return

results field on self.EntriesDf would be a concatenation of prefix-origin-destination-suffix string, which could have long characters. Saving the format as

processing duration

function has built in multithreading capabilities using multiprocessing.Pool. Please take notice of self.baseSet['Threads'], as the value is also used in this function. Larger models, larger search distance, larger detour ratio will be cause exponentially longer processing time.

Parameters

GraphSm : GraphSim required: GraphSim object, initialized, can also be from previous analysis runs.

MeasureDf : DataFrame required

Pandas Dataframe of sets of runs. Make sure in format of columns:
'OriginField' - 'DestinationField' - SeachDistance - WeightField

Note that OriginField and DestinationField columns are matching with the EntriesDF's column name.
Example:

Function	Function.1	Dist	WeightField
ResidentialV	ShopFNB	1200	Capacity
ResidentialV	BusStop	400	Routes
ShopFnB	Office	700	GFA
ShopFnB	Office	1500	GFA

columns

Note that the first and second columns names are matching the self.EntriesDf column names. its rows are the values of the features. Note that Pandas DataFrame cannot have matching/same column name, so if the keys for Origin and Destination are the same, the suffix of '.1' on the columns are appended, which is accounted in the functions.

same destinations

The resulting field is constructed from OptSuffix + DestinationField + OptPrefix, where OptSuffix and prefix can be accessed on the **kwargs settings. If there are 2 or more same destinations, the subsequent result names will be appended '_n', with n representing intergers.

**kwargs : Dict/keys-values

Parameter arguments, that contain:

"OptSuffix" string default ''

suffix string for all output.
"OptPrefix" string default "weight"

prefix string for all outputs.

additional arguments

Additional arguments can be added in this kwargs that are corresponding to the Reach's arguments. For example, CalcType, CalcComp, etc.

Use Example

import SNAPy as sna
import geopandas as gpd
import pandas as pd

dfNetwork = gpd.read_file('testdata\\NetworkClean.shp') # network dataframe
dfEntries = gpd.read_file('testdata\\Features.gpkg', layer='Features') # entrance dataframe

nwSim = sna.GraphSims(dfNetwork, dfEntries, settings)

dfMeasures = pg.read_csv('testdata\\Measures.csv') # routine dataframe

ReachAggregate(nwSim, dfMeasures, OptSuffix='R1_', CalcType='NWD') # results does not need to be contained

nwSim.NetworkDf.to_file("output.gpkg", layer="Entries", crs="EPSG:32748", driver='GPKG') # geoDataFrame can be saved to GIS files

BetweenessPAggregate func

BetweenessPAggregate(GraphSm:GraphSims, PairsDf:DataFrame, MeasureDf:DataFrame=None, **kwargs)

returns GraphSims : GraphSims class object
GraphSims object

Runs a set of Reach functions available on the GraphSim functions. Reads the set of runs from a dataframe. See MeasureDf explanation for input sets. Outputs GraphSims with self.NetworkDf appended with results. PairsDf is required for iteration, but MeasureDf, which is used for interpolating to form simulation weights, does not; if left none, make sure the weight fields already exists on Graphsims.Entries.

PairsDf and MeasureDf

use the same column order as in the parameter documentation, it does not match keys!

processing duration

function has built in multithreading capabilities using multiprocessing.Pool. Please take notice of self.baseSet['Threads'], as the value is also used in this function. Larger models, larger search distance, larger detour ratio will be cause exponentially longer processing time.

Parameters

GraphSm : GraphSim required: GraphSim object, initialized, can also be from previous analysis runs.

PairsDf : DataFrame required

Pandas Dataframe of sets of runs. Make sure in format of columns:
Name - OriginWeightFields - DestinationWeightFields - SearchDistance - DetourR - AlphaExp*
* optional

Note that OriginField and DestinationField columns are matching with the EntriesDF's column name.
Example:

RsltName	OriginWeightField	DestinationWeightField	Distance	DetourR
Commute_01	ComOr01	ComDes	1200	1.0
Commute_02	ComOr02	ComDes	700	1.1
Activity_01	Cap01	CapW01	700	1.5
Event_01	Stadium	PT01	400	1.0

SearchDistance, DetourR, and AlphaExp

these settings are optional, with if columns is not found, will be using default values that can be declared on kwargs.

column order

Use the same column order as the documentation. Naming of those columns does not effect processing

MeasureDf : DataFrame default: None

Pandas Dataframe of sets of runs. Make sure in format of columns:
'TypeMatch' - BaseWeightField - *Extrapolated Fields

For extrapolating columns for betweeness matching with PairsDf. DataFrame can be expanded to n columns/fields.
Example:

Function	WeightField	ComOr01	ComDes	Cap01	CapW01
ResidentialV	Residents	0.8	0.0	0.5	0.5
ShopFNB	Capacity	0.1	0.0	0.8	2.0
ShopFnB	Capacity	0.1	0.0	0.8	2.0
BusStop	RouteW	0	1.0	0.0	0.0
Office	Capacity	0.8	0.0	0.6	0.6

columns

Note that the first column name should match the self.EntriesDf column names. its rows are the values of the features.

**kwargs : Dict/keys-values

Parameter arguments, that contain:

"OptSuffix" string default ''

suffix string for all output.
"OptPrefix" string default "weight"

prefix string for all outputs.

additional arguments

Additional arguments can be added in this kwargs that are corresponding to the Reach's arguments. For example, DetourR, AlphaExp, etc.

Use Example

import SNAPy as sna
import geopandas as gpd
import pandas as pd

dfNetwork = gpd.read_file('testdata\\NetworkClean.shp') # network dataframe
dfEntries = gpd.read_file('testdata\\Features.gpkg', layer='Features') # entrance dataframe

nwSim = sna.GraphSims(dfNetwork, dfEntries, settings)

dfPairs = pg.read_csv('testdata\\Pairs.csv')
dfMeasures = pg.read_csv('testdata\\Measures.csv') # routine dataframe

BetweenessPAggregate(nwSim, dfPairs, dfMeasures) # results does not need to be contained

nwSim.NetworkDf.to_file("output.gpkg", layer="Entries", crs="EPSG:32748", driver='GPKG') # geoDataFrame can be saved to GIS files

@December2023