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Data and codes for the publication: Decay radius of climate decision for solar panels in the city of Fresno, USA

Barton-Henry, Kelsey; Wenz, Leonie; Levermann, Anders

This repository contains core codes and data underlying the analyses and figures from: Barton-Henry, Wenz, and Levermann (2021).The following provides a brief description of the codes and data included.

Data:

final_merged.csv - This file contains the core dataset used the all results and figures. This dataset is a result of the aggregation of the following publicly available datasets:

  • Bradbury, K. et al. Full collection: Distributed solar photovoltaic array location and extent data set for remote sensing object identification. Sci. Data https://doi.org/10.6084/m9.figshare.c.3255643.v2 (2016).
  • The County of Fresno, California. GIS Shapefiles. Fresno County GIS Portal (2020) Retrieved from: https://www.co.fresno.ca.us/ departments/public-works-planning/divisions-of-public-works-and-planning/cds/gis-shapefiles, accessed January 2020.
  • Institute of Education Science National Center for Education Statistics Education Demographic and Geographic Estimates (EDGE). School District Boundaries (2020). Retrieved from: https://nces.ed.gov/programs/edge/Geographic/DistrictBoundaries, accessed March 2020.
  • U.S. Census Bureau. 2009–2013 5-Year American Community Survey Demographic and Housing Estimates (2014). Retrieved from https://data.census.gov/cedsci/table?q=acs&tid=ACSDP5Y2013.DP05&hidePreview=false, accessed November 2020.

Code included, in relevant order:

figure_1.py - This script produces Figure 1, a map showing the geographic area of analysis along with the geolocations of solar panels, addresses, as well as examples of several radii over which panel density is calculated.

figure_2.py - This script produces Figure 2, as well as outputs several dataframes with the normalized panel density variables used in the further analysis of the feature importances scores.  

figure_3.py - Figure 3 is produced, as well as several .csvs containing dataframes with varying panel density radii, constructed with the normalized panel density at one radius subtracted from that of the previous.

figure_4.py - Produces Figure 4, an analysis of the influence of household income. Figures S15 and S17 of the Supplementary Information section are also created. 

models.py - This script builds the three different models tested, and calculates the feature importances scores as well as performance metrics for each. 

confusion_matrices.py - This script creates figures of the confusion matrices created from the performance output for each of the tested models, as well as the model for which panel density is omitted. These are Figures S2, S4, S6, and S7. 

ols.py - This script produces the estimates contained in Tables S7, S8, and S9, which are the results of the OLS analysis.

tract_ave_plot.py - Figures S8 and S9 are produced, showing changes in the feature importances when panel density is averaged over census tract. 

households_all.py - Analysis of the influence of density based on the number of households in each tract, resulting in Figures S11 and S12. 

tract_area_all.py - Creates Figures S13 and S14, providing the analysis of the influence of density across census tracts of varying area sizes. 

hhval_all.py - The analysis of the influence of household value is conducted, and Figure S16 is produced. 

Files (146.8 MB)
Name Size
confusion_matrices.py
md5:7fd05005902a54d4ee66cc3cfa5f0bab
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figure_1.py
md5:a39a726baea2c3781fa5e7b816c2a8c3
5.2 kB Download
figure_2.py
md5:e580e622a302bd4a2b79163fb411e885
16.9 kB Download
figure_3.py
md5:81f971587f60a8aa9fb8f7e8dd08f5ef
14.7 kB Download
figure_4.py
md5:b5efb694cb8e6aef9a6c9f452c7fc852
22.1 kB Download
final_merged.csv
md5:18ee6956cb984df7fdeeb8055a5c7d7b
146.6 MB Download
hhval_all.py
md5:8eee5dab25354abae0939a6467fdc56a
21.2 kB Download
households_all.py
md5:293fefada64253677e467e96542b974b
24.2 kB Download
models.py
md5:ffef1324adbdb18a7b265d58af26de1c
8.3 kB Download
ols.py
md5:c31271d7b2c0bf2e67e42697691d07d4
3.3 kB Download
tract_area_all.py
md5:6d80185340b54128307aabbc05759737
24.8 kB Download
tract_ave_plot.py
md5:27f41e7cd18958d9e4f6d16a8620a5cc
18.0 kB Download
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