Ecometrics: New Methods of Representation for Ecological Footprint and Biocapacity by Angela Crisostomo

Ecometrics New Methods of Representation for Ecological Footprint and Biocapacity

Hsin Yi Chao Richard Chou Angela Crisostomo Columbia GSAPP Leah Meisterlin | Grga Basic Geographic Information Systems

2018 Fall Hsin Yi Chao Richard Chou Angela Crisostomo

Columbia GSAPP Leah Meisterlin | Grga Basic Geographic Information Systems

Contents introduction research questions methodology process findings conclusion citations

Introduction Project Overview Ecometrics perform quantitative analysis to evaluate whether certain activities or features contribute to a more sustainable system of production and consumption. In the context of ongoing climate change and discourse on sustainable development, these metrics are in common use as tools to build policy and advocacy. Our project tests the robustness of two common Ecometrics - Ecological Footprint (EF) and Biocapacity (BC) as regional planning and design tools to critique their applications and limits. We used the Hudson Valley Region as a case study to test these methods. Through GIS, we developed a methodology to map Ecological Footprint and Biocapacity at a regional scale and explored new methods to represent the relationship and nuances of these ecometrics.

Background First developed by Wackernagel and Rees in 1996; Ecological Footprint and Biocapacity are metrics ubiquitous today in science, policy, and popular discourse. We can often find these metrics applied in a Global Scale or a State Scale – comparing the footprints of countries/ populations with each other1 or as an advocacy tool communicating the need for the public’s behavior change. The graphic and spatial qualities of these metrics have also lent themselves to the imagination of designers, planners, and policy makers to develop methods of application for the EF and similar metrics at more local and regional scales. Examples can range from the absurd to the serious – e.g. MVRDV’s “Metacity/ Datatown” and “Pig Cities”, City of Calgary’s Land use and Planning Policy (2008); while very different, these examples showcase EF’s application in the realm of scenario building. The common usage of these ecological metrics and the interest in its application at local and regional scales call for critical exercises – How can we assess the accuracy and relevance of EF? Can it be used at regional and local scales reliably? How do we test the robustness of the EF as a planning tool?

1.e.g. by identifying “Ecological Debtors” and “Ecological Creditors”

Ecometrics “Ecological Footprint” (EF) is a tool based on the idea of measuring the carrying capacity of a given area2; and the idea of a “Biological Capacity” or “Biocapacity” (BC). It is a measure of how much area of productive land it takes to sustain a person or population’s lifestyle. “Biological Capacity” (BC) is a measure of an ecosystem’s capacity to continuously produce resources and to absorb the waste generated by a given person/population.

Ecological Footprint and Biocapacity are usually measured in global hectares (gha), a biologically productive hectare which represents the world average productivity every year. Ecological Footprint and Biocapacity are weighted through “Yield Factors” (YF) and “Equivalence Factors” (EQF) to normalize different land types and locations in the world. These Yield Factors and Equivalence Factors are given metrics that measure carbon emissions, infrastructure, forest, grazing land, cropland, built-up surfaces, and marine/ inland water resources in a given area in the world.

2. Based on the areas of six (6) identified land use types: cropland, grazing land, forest, fishing grounds, built-up land, and forest area, as well as the carbon footprint of the given area (area of forest required to offset human carbon emissions).

CO2 EMISSIONS INFRASTRUCTURE FOREST GRAZING LAND CROPLAND BUILD-UP SURFACES MARINE/ INLAND WATER

Current Methods of Representation

ECOLOGICAL DEFICITS AND RESERVE CAPACITY In current representation, Biocapacity and Ecological Footprints are subtracted from each other to determine whether a given region has a “Ecological Deficit” or an “Ecological Reserve”. An Ecological Deficit occurs when the EF of a given population exceeds the BC of the area available to the population; conversely, an Ecological Reserve occurs when the BC exceeds the EF.

BIOLOGICAL CREDITORS AND DEBTORS Biocapacity and Ecological Footprints are often compared with each other to determine whether a given region is a biological “creditor” or a biological “debtor” at a global scale.

UNIVARIATE CHOROPLETH MAPS To illustrate and map Ecological Footprint and Biocapacity metrics, Univariate choropleth maps are often used to offer side by side comparisons.

Research Questions Critique Scales of Application EF and BC calculations, are often measured in global hectares/ person and applied at a state scale. This scale of application generalizes data across large areas and may not capture the nuances of the data apparent at finer scales. For instance, the latest available information on New York Stateâ&#x20AC;&#x2122;s Ecological Footprint (NFS, 2015) measured NYS Ecological Footprint at 14.2 global acres/ person; the lowest Ecological Footprint in the United States. However, we could reasonably assume that New York City would have a much smaller EF than other counties in New York State due density and lifestyle differences (e.g. transportation and housing) mainly reflected in its carbon footprint. This begs the question of whether these metrics are meaningful or useful as design or planning tools. Would the average of 14.2 global acres/person be meaningful to describe lifestyle and consumption patterns of other counties in the state? To evaluate, we developed a GIS methodology to calculate the EF and BC of sub-counties in the Hudson Valley Region and compared them with the EF and BC of New York City.

Methods of Representation Because scales of application of EF and BC pose questions of accuracy and relevance, we hypothesize that the relationship between the two may be more meaningful than single metrics of each. Univariate choropleth maps are thematic maps that display a single variable of non-location or generalized data. Because they are easy to read, they give immediate readings of a given areaâ&#x20AC;&#x2122;s ecometrics but may not capture the relationships and nuances that exist between Ecological Footprint and Biocapacity. In order to capture these relationships and nuances, the two variables need to be present in a single map simultaneously. We developed a GIS methodology to combine EF and BC into a bivariate map, which displays the variables in a single map by combining two sets of color ramps. Data classification and graphic representation of classified data played a big role in the development of the methodology. The resulting bivariate map enabled us to draw more meaningful evaluations at a regional scale, to compare the ecometrics of Hudson Valley sub counties against New York City.

Questions 1.

How can we use GIS to calculate and map the EF and BC of the Hudson Valley Region?

Is the Hudson Valley Region an ecological debtor or ecological creditor to New York State based on NFA methodologies and available data?

Can EF and BC be used reliably as a regional planning tool?

Hypothesis We hypothesized that we could use existing land cover datasets and the National Footprint Account’s (NFA) methodology to calculate the EF and BC of the Hudson Valley Region. Our initial impression was that the Ecological Footprint of New York State is skewed because New York City is an outlier – having a much smaller EF than the rest of the state because of life-style differences in mobility and housing, mainly reflected in its Carbon Footprint; and that the Hudson Valley Region’s EF and BC would likely be higher (and perhaps more representative) than the current NYS average.

Methodology Step-by-step Methodology

Data Collection

NYS Average Ecological Footprint per person (gha/person)

Data Preparation

Average Carbon Footprint per person

9.3

Average Non-Carbon Footprint per person

5.0

NYS Carbon Footprint per Person by Zip Code (gha/person)

Intersect Zip Code Boundary with Sub Counties Boundary

NYS Census Data on Population per Census Tract (Polygons)

Feature Fields “POP10” to Points

NYS Census (Points)

NYS Sub County Boudary (Polygons) Select & Export Counties in Hudson Valley

NYS County Boudary (Polygons)

Union

Hudson Valley Boundary (Polygons)

Sub Counties Boundary in Hudson Valley (Polygons)

Intersect “Hudson Valley” Polygon with Land Cover

Yield Factors (YF)

NYS Land Cover (Raster)

Caculate Factors (Table)

Countries in Hudson Valley (Polygons)

Intersect “Hudson Valley Sub Counties” Polygon with Land Cover

Cropland

1.22088

Grazing Land

0.723748

Fishing

1.0

Forest

1.24176

Cropland

1.22088

Hudson Valley Land Cover (Raster)

Raster to Polygons

Calculations

Zip Code Area within Sub Counties (Polygons) Spatial Join

Calculate Total Carbon Footprint of Sub Counties

Carbon Footprint of Sub Counties

Table Join

Calculate Total Carbon Footprint of Sub Counties

Carbon Footprint of Sub Counties

Classification and Representation

Calculate Carbon Footprint/pers on of Sub Counties

Carbon Footprint/pers on of Sub Counties

LOW

Inland Water Area

LOW

EV K EA BR

BIOCAPACITY

Grazingland

HIGH

Cropland

ECOLOGICAL FOOTPRINT

HIGH

Forest Spatial Join Infrastructure

Table Join “Factors Dataset” with “Hudson Valley Land Cover”

Intersect “Hudson Valley Sub Counties” with “Land Cover”

Land Cover of Each Sub County (201) in Hudson Valley

Calculate Summarize Biocapacity by Land Cover Sub Counties' Biocapacity within sub Counties

Calcuate Biocapacity per person of Sub Counties (ha/person)

Biocapacity per person of Sub Counties

Data Collection Required Data and Data Sources 1.

NLCD Homer, C.G., Dewitz, J.A., Yang, L., Jin, S., Danielson, P., Xian, G., Coulston, J., Herold, N.D., Wickham, J.D., and Megown, K., 2015, Completion of the 2011 National Land Cover Database for the conterminous United States-Representing a decade of land cover change information. Photogrammetric Engineering and Remote Sensing, v. 81, no. 5, p. 345-354 <https://www.mrlc.gov/nlcd2011.php>

2. Appropriated Production Equivalence Factors (EQF) and Production Yield Factors (YF) Michael Borucke, David Moore, Gemma Cranston, Kyle Gracey, Katsunori Iha, Joy Larson, Elias Lazarus, Juan Carlos Morales, Mathis Wackernagel, Alessandro Galli., Ecological Indicators, Volume 24, January 2013, Pages 518-533. Accounting for demand and supply of the biosphereâ&#x20AC;&#x2122;s regenerative capacity: The National Footprint Accountsâ&#x20AC;&#x2122; underlying methodology and framework <https://www. sciencedirect.com/science/article/pii/S1470160X12002968> 3.

NYS Average Ecological Footprint Per Person (gha) Christopher M. Jones and Daniel M. Kammen. Cool Climate, Berkeley. Spatial Distribution of U.S. Household Carbon Footprints Reveals Suburbanization Undermines Greenhouse Gas Benefits of Urban Population Density, 2013. <http://coolclimate.berkeley.edu/maps>

NYS Carbon Footprint by Zipcode Christopher M. Jones and Daniel M. Kammen. Cool Climate, Berkeley. Spatial Distribution of U.S. Household Carbon Footprints Reveals Suburbanization Undermines Greenhouse Gas Benefits of Urban Population Density, 2013. <http://coolclimate.berkeley.edu/maps>

5. Census Data on Population per Census Tract American Factfinder. ACS 5 Year Estimates, 2017. United States Census Bureau. < https://factfinder.census.gov/faces/tableservices/jsf/pages/ productview.xhtml?src=CF>

Zip Code Scale Data

Census Tract Scale Data

Sub County Scale Data

County Scale Data

Regional Scale Data

Limitation: Carbon Footprint data is available by zipcode, however, non-carbon Footprint data is only available at a state scale, we combined both data at a sub county scale.

State Scale Data

Methodology reflects working and visualizing data at various scales.

Calculating Ecological Footprints Methodology To evaluate Hudson Valley Regionâ&#x20AC;&#x2122;s ecometrics against New York City and with New York State, we first prepared and mapped the necessary data using sub county boundaries and scale. This scale allowed us to get a better geographic reading of the Hudson Valley and an appropriate scale to compare city regions. Due to limited data in flows of appropriated production and land appropriation (yields) in the Hudson Valley, we obtained data from the Cool Climate Network on Carbon and Non-Carbon Footprints. For New York State, data on Carbon Footprint per person is available by zip code. We intersected this data with the sub counties boundary to map the average Carbon Footprint of each sub county in Hudson Valley. We joined this with available data on average Non-Carbon Footprints in New York State to obtain the total Ecological Footprint (Carbon Footprint + Non-Carbon Footprint) for each sub county in Hudson Valley. We then calculated the Eco Footprint per Capita for each sub county using available Census 2010 Population data by Block. The resulting univariate choropleth map correlates with the population by county map. Sub counties with a low population have a higher carbon footprint while sub counties with higher populations (such as those in New York City) reflect the least carbon footprint.

Agricultural products

WorldC rop Yields

Livestock products

WorldG rass Yields

Fishery& Aquaculture

WorldF ish Yields

Timber products

WorldF orest Yields

CO2 emissions

Carbon Uptake Factor

Built-ups urfaces

Ecological Footprint

worlda verage yield [w ha]

= = = = = =

[g ha]

Cropland

Cropland EQF

GrazingL and

Grazing EQF

FishingG round

Fishing EQF

Forest Land

Forest EQF

Carbon UptakeL and

Forest EQF

Built-upL and

Cropland EQF

= = = = = =

Limitation:

Cropland Footprint GrazingF ootprint FishingF ootprint Forest Footprint CO2 Footprint Infrastructure Footprint

Total Ecological Footprint

[t /t /ha]

/ / / / /

No data available on flows of appropriated production and land appropriation yields. NFA methodology substituted for Global Footprint Network methodology and available data.

Eco Footprint per Capita

Total Population by Sub County

Total Eco Footprint

Census 2010 Population by Block

Intersected Boundaries

Hudson Valley Sub County Boundaries

Carbon Footprint per Capita by Zipcode

shingY F

Forest YF

ropland YF

In calculating the Biocapacity of the Hudson Valley Region, we prepared and mapped the necessary data once again using sub county boundaries and scale. We used available land cover raster data from the National Landcover Database and the National Footprint Accountâ&#x20AC;&#x2122;s (NFA) methodology to calculate Biocapacity the Biocapacity of the region. [g ha]

= = = = =

Biocapacity for cropland, grazing land, marine/inland cover area, forest area, Cropland and built infrastructure area were each calculated and mapped using sub countyBiocapacity boundaries, and subsequently with Census 2010 Population data by Block. Grazing Biocapacity The resulting univariate choropleth map describes Biocapacity per Capita for each sub county in the Hudson Valley Region. Marine/Inlandw ater Biocapacity

Forest Biocapacity Cropland Biocapacity

Biocapacity [g ha]

[ha]

Cropland Area

Cropland YF

Grazing Land Area

Grazing YF

Marine/Inland water Area

FishingY F

Forest Area

Forest YF

Infrastructure Area

Cropland YF

= = = = =

Cropland Biocapacity Grazing Biocapacity Marine/Inlandw ater Biocapacity

Forest Biocapacity Cropland Biocapacity

TotalB iocapacity

Grazing YF

Methodology

TotalB iocapacity

ropland YF

Calculating Biocapacity

Biocapacity per Capita

Total Population

Total Biocapacity

Cropland Biocapacity

Grazing Land Biocapacity

Inland Water Biocapacity

Forest Land Biocapacity

Infrastructure Land Biocapacity

Data Classification Classification and Relationship Definition Bivariance Classification As there is no built-in two-variable reclassification process in ArcGIS, we developed a field calculation script via python to create a third classification field with the bivariate classifications. The first steps of creating a bivariate classification is creating a bivariance legend. The two scales are selected to share the same neutral grey as its lowest classification as the common base for the color matrix. To avoid confusion, the Biocapacity scale is classified from A-D, whereas the Eco Footprint is classified from 1-4. The resulting Eco Footprint (EF) and Biocapacity (BC) layers from the last step were classified through their respective single-variate choropleth maps. Taking the break points from the univariance classification we created three custom fields: EF Ranking, BC Ranking, and Combined Ranking to record each sub countyâ&#x20AC;&#x2122;s respective classification. The final bivariance classification consists of 16 classes in a 4x4 matrix (A1, A2, A3...etc.)

Relationship Definition While a bivariance choropleth matrix is useful in displaying relationships between Biocapacity (BC) and Eco Footprint (EF), the number of classifications is potentially confusing. To add clarity, we defined the four corners of the bivariance matrix as they represent the most interesting conditions between the two variables (figure X). These corners represent either strong leaning to a single variable (high BC, low EF) or strong agreement between the two (high BC, high EF). In addition, the diagonal line across the matrix is labeled as areas with a balanced relationship.

Graphic Representation Regional Performance Map and Biological Performance Data Distribution & Classification Type The first set of maps show the relative Eco Footprint and Biocapacity relationship between the sub counties of the Hudson Valley. One initial realization during the classification process is that the Biocapacity per Capita data contains multiple outliers, therefore skewing the data distribution. Specifically, sub counties that are within the Catskills Mountains region has a much higher Biocapacity than the rest of the valley. Thus, we have chosen to represent the two variables through quantiles. In the maps below are classified in 4 quantiles, where the sub counties are ranked in 25% intervals. As we are interested in the relative placement of each sub county within the Hudson Valley and New York City context, we believe that this classification would result in a more meaningful bivariance choropleth map.

Ecological Ecological Footprint(gha)/Capita Footprint(gha)/Capita 13.34 13.34 - 18.88 - 18.88

0.00 0.00 - 0.32 - 0.32

18.88 18.88 - 20.40 - 20.40

0.32 0.32 - 1.03 - 1.03

20.40 20.40 - 22.42 - 22.42

1.03 1.03 - 2.75 - 2.75

22.42 22.42 - 44.76 - 44.76

2.75 2.75 - 106.9 - 106.9

Ecological Ecological Footprint(gha)/Capita Footprint(gha)/Capita

Biocapcity(gha)/Capita Biocapcity(gha)/Capita

0 -04- 4

4 -416 - 16

1616 - 33 - 33

3333 - 47 - 47

3333 - 107 - 107

Ecological Ecological Footprint(gha)/Person Footprint(gha)/Person

Biocapcity(gha)/Capita Biocapcity(gha)/Capita

Biocapcity(gha)/Person Biocapcity(gha)/Person

13.34 13.34 - 18.88 - 18.88

18.88 18.88 - 20.40 - 20.40

20.40 20.40 - 22.42 - 22.42

22.42 22.42 - 44.76 - 44.76

Regional Relationship Map The following map shows the relative performance of Eco Footprint and Biocapacity per capita by sub county within the Hudson Valley, displayed in a bivariance choropleth map.

Farmland

Sprawl

Urbanized Area

Biocapcity(gha)/Person

2.75 - 106.9

1.03 - 2.75

0.32 - 1.03

0.00 - 0.32

13.34 - 18.88

18.88 - 20.40 20.40 - 22.42

22.42 - 44.76

Ecological Footprint(gha)/Person

Biological Creditor/ Debtor/ Break-even The second set of maps show the Biological Balance of each sub county via the difference between Ecological Footprint and Biocapacity. In reference to the representation methodology by of Global Footprint Network, which defines regions as Biological Creditors or Debtors, we produced a second bivariance matrix using identical Equal Interval breakpoints between both variables.

Ecological Footprint(gha)/Capita Ecological Footprint(gha)/Capita

Biocapcity(gha)/Capita Biocapcity(gha)/Capita

0.00 - 0.32 - 18.88 In contrast to13.34 Quantile classification, Equal Interval breakpoints produced less 0.00 - 0.32 13.34 - 18.88 visually interesting maps. However, the combined bivariance map was able 18.88 - 20.40 0.32 - 1.030.32 - 1.03to 18.88 - 20.40 show not only whether each sub county was a Biological Creditor or1.03 Debtor, - 2.75but 20.40 - 22.42 1.03 - 2.75 20.40 - 22.42 also22.42 the scale of its difference, as the matrix shows the multiple scenarios of a 2.75 - 106.9 22.42 - 44.76 2.75 - 106.9 - 44.76 same Biological Balance.

Ecological Footprint(gha)/Capita Ecological Footprint(gha)/Capita

Biocapcity(gha)/Capita Biocapcity(gha)/Capita

0-4

4 - 16

16 - 33

33 - 47

33 - 107

Ecological Footprint(gha)/Person Ecological Footprint(gha)/Person

Biocapcity(gha)/Person Biocapcity(gha)/Person

13.34 - 18.88 13.34 - 18.88 18.88 - 20.40 18.88 - 20.40

20.40 - 22.42 20.40 - 22.42 22.42 - 44.76 22.42 - 44.76

Ecological Footprint(gha)/Person Ecological Footprint(gha)/Person

Biocapcity(gha)/Person Biocapcity(gha)/Person

13.34 - 18.88 13.34 - 18.88 18.88 - 20.40 18.88 - 20.40

20.40 - 22.42 20.40 - 22.42 22.42 - 44.76 22.42 - 44.76

26 Ecological Footprint(gha)/Person Ecological Footprint(gha)/Person 13.34 - 18.88 13.34 - 18.88

Biocapcity(gha)/Person Biocapcity(gha)/Person 13.34 - 18.88 13.34 - 18.88

Regional Relationship Map The following map shows the Biological Balance calculated from the difference between Eco Footprint and Biocapacity per capita by sub county within the Hudson Valley, displayed in a bivariance choropleth map.

Catskills Mountains Region

107.0 33.0 16.0

Biocapcity(gha)/Person

4.0

0.00 0.0

4.0

16.0

33.0

47.0

Ecological Footprint(gha)/Person

Findings Scales and Aggregation

Subcounty Scale

County Scale

Regional Scale

Scales and Aggregation

107.0 33.0

Biocapacity is dependent on landcover data while Eco Footprint is dependent on lifestyle in a given area, as aggregation increases based on the scale of sampling, information becomes more and more generalized, and less meaningful.

16.0

Biocapcity(gha)/Person

4.0

In evaluating EF and BC at a regional scale applying data at a sub county level better illustrates geographic and spatial relationships. Finer scales such as by zipcode may likely yield the same resulting colors. 0.00 0.0

4.0

16.0

33.0

Ecological Footprint(gha)/Person

47.0

NYS EF = 15.20 BC = 3.50

HUDSON VALLEY EF = 18.70 BC = 0.68

NYC EF = 14.01 BC = 0.01

Methods of Representation and Performance UNIVARIATE CHOROPLETH MAPS To illustrate and map Ecological Footprint and Biocapacity metrics, Univariate choropleth maps are often used to offer side by side comparisons.

BIVARIATE CHOROPLETH MAPS A bivariate map can better illustrate relationships in data by combining two distinct features into a single map.

BALANCE OF TOTAL EF + BIOCAPACITY BIVARIATE MAP

BALANCE OF AVERAGE EF + BIOCAPACITY BY CAPITA BIVARIATE MAP 31

Conclusion GIS can be an effective tool to map EF and BC at various scales. Existing Land Cover Data can be used with relatively simple geoprocessing operations to produce single feature univariance maps. However, current methods of representing EF and BC, limit the ability of the maps to communicate the nuances between EF and BC relationships and limit the maps ability to draw meaningful insight on geographic and spatial features, which can only be drawn at less aggregated scales - e.g. for regional mapping, at a sub county scale. Hence, aggregated EF and BC applied at large scales do not provide the geographic, spatial, and nuanced representation of data necessary as planning or design tools. Bivariate mapping can better used to illustrate the degrees of difference within a given datasets â&#x20AC;&#x153;breaking pointâ&#x20AC;?, and when applied at the appropriate scale, can better illustrate geographic and spatial relationships. For instance, a bivariate map of EF and BC per capita of the United States better illustrates the range of High BC areas within the Mississippi River Basin region. These High BC areas exhibit a range of Low EF - High EF areas. In studying the Hudson Valley Region at a sub county scale, ecological debtors and ecological creditors are better illustrated geographically and spatially. For instance, the Catskill mountains, displayed in blue, indicate that the area has the highest EF and BC in the region, and is one of the few counties in the region that are ecological creditors. Bivariate EF and BC maps executed at the proper scales, provide better alternatives to single feature univariate maps, and when executed with the right data, be a more useful tool for regional planning and design application.

RESULTING EF AND BC BY CAPITA

MISSISSIPPI RIVER BASIN HYDROLOGICAL MAP

Citations MISSISSIPPI WATERSHED USGS. (2017). Watershed boundaries for study sites of the U.S. Geological Survey Surface Water Trends project. [online] Available at: https://www.sciencebase.gov/catalog/ item/57a9e239e4b05e859be05534 [Accessed 11 Dec. 2018]. DEFINITIONS Footprintnetwork.org. (2018). Glossary - Global Footprint Network. [online] Available at: https://www.footprintnetwork.org/resources/ glossary/ [Accessed 02 Dec. 2018]. IMAGE YouTube. (2016). National Footprint Accounts – Ecological Balance Sheets for 180+ Countries. [online] Available at: https://www.youtube. com/watch?v=_T5M3MiPfW4 [Accessed 02 Dec. 2018]. CURRENT REPRESENTATION Data.footprintnetwork.org. (2018). Open Data Platform. [online] Available at: http://data.footprintnetwork.org/#/? [Accessed 02 Dec. 2018]. US ECOLOGICAL FOOTPRINT AND BIOCAPACITY MAP News.nationalgeographic.com. (2015). Is Your State Consuming More Than Nature Can Provide?. [online] Available at: https://news. nationalgeographic.com/2015/07/biocapacity-and-ecological-footprint/ [Accessed 02 Dec. 2018]. FORMULA Borucke, M., Moore, D., Cranston, G., Gracey, K., Iha, K., Larson, J., Lazarus, E., Morales, J., Wackernagel, M. and Galli, A. (2013). Accounting for demand and supply of the biosphere’s regenerative capacity: The National Footprint Accounts’ underlying methodology and framework. [online] Columbia University Libraries. Available at: https://www.sciencedirect.com/science/article/pii/S1470160X12002968 [Accessed 13 Nov. 2018]. BIVARIATE METHOD Joshuastevens.net. (2015). Joshua Stevens - Bivariate Choropleth Maps: A How-to Guide. [online] Available at: http://www.joshuastevens. net/cartography/make-a-bivariate-choropleth-map/ [Accessed 13 Nov. 2018].