This repository provides code and data used in "Social Equity of Bridge Management" (DOI: 10.1061/JMENEA/MEENG-5265). Both the dataset used in the analysis ("Panel.csv") and the R script to create the dataset ("Panel_Prep.R") are provided. The main results of the paper as well as alternate specifications for the ordered probit with random effects models can be replicated with "Models_OrderedProbit.R". Note that these models take an extensive amount of memory and computational resources. Additionally, we have provided alternate model specifications in the "Robustness" R scripts: binomial probit with random effects, ordered probit without random effects, and Ordinary Least Squares with random effects. An extended version of the supplemental materials is also provided.

The Gentrification, Urban Interventions, and Equity (GENUINE) Tool is a map-based gentrification tool for Canadian metro areas. The primary envisioned use for GENUINE is policy evaluation and research studies that assess the potential health and societal impacts of gentrification processes. Accompanying maps for each Census Metropolitan Area (CMA) are published online. The mapping platform simultaneously shows four gentrification measures for each CMA, facilitating comparisons between measures. We developed GENUINE to integrate gentrification measures relevant to the Canadian context, adapting four published gentrification measures that have been applied to U.S. and Canadian census data. We computed these measures for all 36 Canadian CMAs to illustrate where gentrification occurred between 2006 and 2016. The GENUINE data were developed by Caislin Firth, Benoit Thierry, Daniel Fuller, Meghan Winters, and Yan Kestens, as part of the gentrification workgroup of INTErventions, Research, and Action in Cities Team with funding from the Canadian Institutes of Health Research. GENUINE measures have been developed using 2006 and 2016 census tract areas to identify areas that have gentrified by 2016. Please refer to the Technical Document for methods used to calculate each gentrification measure. GENUINE data were linked to all 2016 six-digit DMTI spatial single link postal code locations in Canada by CANUE staff. NOTE: all postal codes within a Census Tract are assigned the same value. Summing data is not appropriate. Original census tract data can be downloaded directly from links provided in the technical documentation.

The relevance of the distribution and accessibility of public services is a key factor in the development of socio-territorial equity in the city. Especially in view of the increasing inequality, polarisation, exclusion and social isolation. Accessibility to public services has been highlighted as a fundamental element for both the development of social interactions and cultural activities in the city. Thus, social infrastructure (SI) acquire a special importance like those services that contribute to the development of socialisation, interaction and social integration in the city. Based on these issues, the data contained in this repository can be used to develope an index of accessibility to the SI in Barcelona at the metropolitan level. A scale of analysis so far little used. Utilizing gravitational models, OpenStreetMap and the use of Principal Component Analysis (PCA) we have developed an index of walking accessibility to the SI. Additionally, we utilize spatial statistical tools that reinforce the results of contextual studies like this one. The data is ready to be used in the R free software environment.

This dataset includes supporting information and spatial data for a Philippines case study on social equity in marine systematic conservation planning (SCP). The study focuses on three dimensions of social equity (recognition, procedure, and distribution) in relation to planning marine protected areas (MPAs) under a SCP framework. It documents how social equity dimensions can be addressed within the planning stages of SCP and compares three planning scenarios to investigate how recognition and procedure equity can impact MPA plans in terms of distribution equity, ecological representation, and spatial efficiency. The study region is located in Sogod Bay in Southern Leyte, Philippines and encompasses the coastal waters of six municipalities: Pintuyan, San Francisco, Liloan, Malitbog, Padre Burgos, and Limasawa. Biodiversity and fishery data were collected using remote sensing and participatory mapping methods, respectively. The data were integrated into the spatial prioritization tool, Marxan with Zones, to develop and compare three planning scenarios.

The supporting information file contains supplemental information on (1) biodiversity data and remote sensing analyses; (2) fishery data and participatory mapping workshops; (3) existing MPA system in the study region; and (4) Marxan with Zones analyses including (a) summary tables of the final Marxan with Zones analysis of each planning scenario, (b) input and output files of Marxan with Zones analyses by scenario, and (c) maps of Marxan with Zones outputs under the three scenarios.

The spatial data file includes SHP and KML files on (1) the administrative boundaries for (a) barangays/communities, (b) municipalities, and (c) municipal waters in the study region; (2) (a) the amount of each biodiversity feature in each planning unit, (b) the distribution of coastal habitats derived from a remote sensing analysis of three WorldView-2 images, (c) areas with cloud cover where habitat classes could not be derived from the remote sensing analysis; (d) mangrove data derived from secondary data sources; (3) the amount of each fishery feature in each planning unit at (a) a barangay/community scale and (b) a municipal scale; and (4) (a) the location and attribute information on existing MPAs in 2015, (b) planning units selected in the MPA zone of the “best” Marxan with Zones solution (lowest score of 100 runs) by scenario, and (c) planning unit selection frequencies for the MPA zone by scenario.

Note Barangay/community names have been replaced with codes to maintain the confidentiality agreement of this study.