Spatial

Spatial#

Space is defined as a network of Location and Linkages. Generally, these can be viewed as nodes and edges. The caveat being that Linkages do behave like Locations in some ways as they having independent activity and spatial attributes such Resource limitations, e.g. Land.

Note: In the case of Resource mapping incurred due to Transport, currently a simplifying assumption is used where streams, e.g. consume, release, all happen at the source node. This assumption is being reviewed. The problem however is solved for across the network, allowing the solutions to converge

Defining Locations#

Locations are point objects in which operations such as Process and Storage can reside. These are straightforward to declare as shown in the example below where Goa is declared as the sole Location. Note that the single location itself serves as the network

from energia import Model, Location

m = Model()
m.Goa = Location(label='Goa, India')
m.network

Let’s declare another Location

m.Pune = Location(label='Pune, India')
m.network

ntw

Given that these locations are on the same level, a network is generated with Pune and Goa as composite locations.

m.network.has

(Pune, Goa)

Nesting#

Locations can be nested, take the following example

from energia import Model, Location

m = Model()
m.Houston = Location(label='Houston, USA')
m.CStat = Location(label='College Station, USA')
m.TX = m.Houston + m.CStat
m.TX.label = 'Texas, USA'
m.network

TX

In this case since an all encompassing Location exists, that is set as the network

m.TX.has

(CStat, Houston)

The structure can be layered

from energia import Model, Location

m = Model()

m.Houston = Location(label='Houston, USA')
m.CStat = Location(label='College Station, USA')
m.TX = m.Houston + m.CStat
m.TX.label = 'Texas, USA'


m.SD = Location(label='San Diego, USA')
m.LA = Location(label='Los Angeles, USA')
m.Cali = m.SD + m.LA
m.Cali.label = 'California, USA'

m.USA = m.TX + m.Cali
m.USA.label = 'United States of America'

m.Athens = Location(label='Athens, Greece')
m.Amsterdam = Location(label='Amsterdam, Netherlands')
m.EU = m.Athens + m.Amsterdam
m.EU.label = 'European Union'

m.Pune = Location(label='Pune, India')
m.Goa = Location(label='Goa, India')
m.India = m.Pune + m.Goa
m.India.label = 'India'

m.Earth = m.USA + m.EU + m.India
m.Earth.label = 'Our Home'

Location.tree provides a dictionary displaying the structure of the network

m.Earth.tree

{EU: {Amsterdam: {}, Athens: {}},
 USA: {Cali: {LA: {}, SD: {}}, TX: {CStat: {}, Houston: {}}},
 India: {Goa: {}, Pune: {}}}

Constituent and Parent Locations#

The parent can be seen using Location.isin

m.Goa.isin

India

Location.has only shows locations one order higher in disposition

m.USA.has

(Cali, TX)

Location.alsohas only shows all nested locations

m.USA.alsohas

(LA, SD, CStat, Houston)

Defining Linkages#

There are two ways to declare Linkage objects.

1. Single Linkage Between Locations#

If there is a single Linkage between two given Locations, Model.Link is more convenient. Linkages are always one directional. To access these linkages when providing data location_a - location_b or vice versa can be used

from energia import Model, Location, Linkage

m = Model()
m.Goa = Location(label='Goa, India')
m.Pune = Location(label='Pune, India')

m.Link(m.Goa, m.Pune)
m.linkages

[Goa-Pune]

2. Bi-directional Linkage Between Locations#

If bi=True is provided as an input, a second independent Linkage is created

from energia import Model, Location, Linkage

m = Model()
m.TX = Location(label='Texas, USA')
m.CA = Location(label='California, USA')

m.Link(m.TX, m.CA, bi=True)
m.linkages

[TX-CA, CA-TX]

3. Multiple Linkages Between Locations#

If multiple linkages exist between the same two locations, these need to be explicitly named and provided

from energia import Model, Location, Linkage, misc_units

m = Model(init=[misc_units])

m.USA = Location(label='USA')
m.IND = Location(label='India')

m.sea = Linkage(source=m.IND, sink=m.USA, dist=1400)
m.air = Linkage(source=m.IND, sink=m.USA, dist=8000, bi=True)
m.linkages

[sea, air, -air]

The sea route is created in one direction. Two one direction linkages are created for the air route. The negative Linkage can be accessed using -Linkage as shown

-m.air

-air

Negative linkages can also be declared as needed

m.sea_back = -m.sea
m.space.linkages  # same as Model.linkages

[sea, air, -air, sea_back]

The sibling linkage can be checked

m.sea.sib

sea_back

Ascertaining Connectivity#

Users can query Energia to see if two locations are connected. For a simple boolean answer, use Location.connected()

m.USA.connected(m.IND)

True

For more comprehensive information, use Location.links()

m.USA.links(m.IND)

IND is source and USA is sink in sea
IND is source and USA is sink in air
USA is source and IND is sink in -air
USA is source and IND is sink in sea_back

[sea, air, -air, sea_back]
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