flowmachine.features.location.pwo¶

Source: flowmachine/features/location/pwo.py

The population-weighted opportunities model uses population distributions for predicting the probability of movement between populated areas. The model computes the attraction between populated centers relative to the distance between each origin and destination locations and also to all other equidistant locations from the destination. The model works similarly to the Radiation Model. The original publication suggests that its ideal usage is in cities.

Class PopulationWeightedOpportunities¶

PopulationWeightedOpportunities(start: str, stop: str, *, spatial_unit: Union[flowmachine.core.spatial_unit.LonLatSpatialUnit, NoneType] = None, departure_rate: Union[pandas.core.frame.DataFrame, float] = 0.1, hours: Union[str, Tuple[int, int]] = 'all', method: str = 'last', table: Union[str, List[str]] = 'all', subscriber_identifier: str = 'msisdn', subscriber_subset: Union[flowmachine.core.query.Query, NoneType] = None)

Source: flowmachine/features/location/pwo.py

Population-weighted opportunities model ¹. The model predicts the mobility between populated areas in cities based only on the population densities of those areas, their spatial distribution, and the number of people that depart a certain area. This model is useful for studying mobility pattern in cities.

Parameters¶

start: str

ISO format date string denoting the first day of data to include
stop: str

ISO format date string denoting the last day of data to include
spatial_unit: typing.Union[flowmachine.core.spatial_unit.LonLatSpatialUnit, NoneType], default None

Spatial unit to which subscriber locations will be mapped. See the docstring of make_spatial_unit for more information.
departure_rate: typing.Union[pandas.core.frame.DataFrame, float], default 0.1

Either one uniform departure rate, or a dataframe with a rate column and columns matching those of the spatial unit. If the latter, results are only returned for locations in this dataframe
hours: typing.Union[str, typing.Tuple[int, int]], default all

The hours of the day to include activity in
method: str, default last

Method used to resolve a daily location
table: typing.Union[str, typing.List[str]], default all

Specify which event types to include. 'all', all available event types are included. Otherwise, should be a (schema qualified) list of events tables.
subscriber_identifier: str, default msisdn

Either msisdn, or imei, the column that identifies the subscriber.
subscriber_subset: typing.Union[flowmachine.core.query.Query, NoneType], default None

If provided, a query or table which has a column with a named subscriber to limit results to.

Examples¶

p = PopulationWeightedOpportunities('2016-01-01', '2016-01-07', departure_rate=pd.Dataframe([{"site_id":'0xqNDj', "rate":0.9}]))

One can also run the model with uniform departure rates for all locations as follows:

PopulationWeightedOpportunities('2016-01-01', '2016-01-07', departure_rate=0.5).head()
    origin  destination  prediction  probability
0  0xqNDj        8wPojr    0.384117     0.010670
1  0xqNDj        B8OaG5    0.344384     0.009566
2  0xqNDj        DonxkP    0.715311     0.019870
3  0xqNDj        zdNQx2    0.267854     0.007440

Where prediction is the absolute number of people that move from one location to another. (This should be interpreted as a integer, but floats are provided for evaluating results in a continuous scale.) And probability is the predicted value over the total population leaving the origin (T_i). That is, how likely it is that a person leaving the origin will be found in a given destination.

Methods¶

cache¶

cache

Source: flowmachine/core/query.py

Returns¶

bool

True is caching is switched on.

column_names¶

column_names

Source: flowmachine/features/location/pwo.py

Returns the column names.

Returns¶

typing.List[str]

List of the column names of this query.

column_names_as_string_list¶

column_names_as_string_list

Source: flowmachine/core/query.py

Get the column names as a comma separated list

Returns¶

str

Comma separated list of column names

dependencies¶

dependencies

Source: flowmachine/core/query.py

Returns¶

set

The set of queries which this one is directly dependent on.

fully_qualified_table_name¶

fully_qualified_table_name

Source: flowmachine/core/query.py

Returns a unique fully qualified name for the query to be stored as under the cache schema, based on a hash of the parameters, class, and subqueries.

Returns¶

str

String form of the table's fqn

index_cols¶

index_cols

Source: flowmachine/core/query.py

A list of columns to use as indexes when storing this query.

Returns¶

ixen: list

By default, returns the location columns if they are present and self.spatial_unit is defined, and the subscriber column.

Examples¶

daily_location("2016-01-01").index_cols
[['name'], '"subscriber"']

is_stored¶

is_stored

Source: flowmachine/core/query.py

Returns¶

bool

True if the table is stored, and False otherwise.

query_id¶

query_id

Source: flowmachine/core/query.py

Generate a uniquely identifying hash of this query, based on the parameters of it and the subqueries it is composed of.

Returns¶

str

query_id hash string

query_state¶

query_state

Source: flowmachine/core/query.py

Return the current query state.

Returns¶

QueryState

The current query state

query_state_str¶

query_state_str

Source: flowmachine/core/query.py

Return the current query state as a string

Returns¶

str

The current query state. The possible values are the ones defined in flowmachine.core.query_state.QueryState.

table_name¶

table_name

Source: flowmachine/core/query.py

Returns a uniquename for the query to be stored as, based on a hash of the parameters, class, and subqueries.

Returns¶

str

String form of the table's fqn

Yan X-Y, Zhao C, Fan Y, Di Z, Wang W-X. 2014 "Universal predictability of mobility patterns in cities". J. R. Soc. Interface 11: 20140834. http://dx.doi.org/10.1098/rsif.2014.0834 ↩