Traccess#
Transportation access and equity computations.
Traccess offers a set of fast and convenient functions to calculate multiple transport accessibility measures. Given a pre-computed travel cost matrix, and using data sets on land use supply, demand, and demographics, the package computes accessibility levels using multiple accessibility measures, such as: cumulative opportunities, minimum travel cost to closest n number of activities, gravity-based (with different decay functions) and different floating catchment area methods.
The package also contains a number of different methods for computing distributive and sufficientarian equity measures to compare the level of access provided across demographic groups.
Installation#
Traccess is currently available via PyPI
pip install traccess
Computing Access to Opportunities#
Computing accecss to opportunities requires both a supply (land use) source containing area-coded counts of locations, and a cost matrix which represents the cost of travel from any zone to any other.
You can specify ID columns on load to use arbitrary values as origins and destinations.
from traccess import AccessComputer, Cost, Supply
supply = Supply.from_csv("supply.csv", id_column="id")
cost = Cost.from_csv("costs.csv", from_id="o", to_id="d")
ac = AccessComputer(supply, cost)
access = ac.cost_to_closest(cost_column="c", supply_columns=["oj2"], n=2)
print(access.data)
Computing Transport Poverty Measures#
from traccess import AccessComputer, Cost, Demographic, EquityComputer, Supply
supply = Supply.from_csv("supply.csv", id_column="id")
cost = Cost.from_csv("costs.csv", from_id="o", to_id="d")
demographics = Demographic.from_csv("demographics.csv", id_column="dd")
ac = AccessComputer(supply, cost)
access = ac.cost_to_closest(cost_column="c", supply_columns=["oj2"], n=2)
ec = EquityComputer(access, demographics)
fgt_df = ec.fgt_poverty(access_column="oj", poverty_line=10.0, alpha=1)
print(fgt_df)