Cities

Infrastructure Assembly Emergy UEVs

The scope of the dataset is 222 cities in the Mercer’s quality of living (QoL) ranking 2019. The photos were retrieved from Flickr.com. To detect the objects and features of every photo, the authors used Google Cloud Vision, which is a recent technology that collects, analyses, and extracts information from visual images. All photos of the 222 cities were processed to detect their labels. The authors conducted latent Dirichlet allocation (LDA) modelling, which is the most common feature extraction or topic modelling algorithm in machine learning. Each photo was assigned the city image dimension including cityscape, landscape, architecture, transport, and recreation. The table shows (1) the identification number of the city ordered by the Mercer’s QoL Ranking (2) the name of the photo file from 1-1,000, (3) the concatenated label of each photo, (4) the number of labels, (5) the Flickr ID of the photo, (6) the owner ID, (7) the type of license, (8) the date that the photo was taken, (9) view count, (10) latitude, (11) longitude, (12) LDA results, (13) the city image dimension.

The data of reported cases of displacements in Nigeria is presented in tabular matrix with, on the one hand, nested rows designated as six Sub-regions, 36 States and Abuja FCT, and Case Location/Local Government Area, and, on the other, 14 columns of variables. The rows were itemised correspondingly as follows: Type of infrastructure project; Mode of displacement; Status of displacement; Mechanisms of displacement; Reasons given for displacement; Project funder/initiator; Type of displacement; Number of people/persons affected; Reported social characteristics of people affected; Response of those affected; Action(s) Outcome; Source(s) of information and link; Date(s) reported; and Notes.

The data repository presented here is related to research article titled “Evicting the poor in the ‘overriding public interest’: Crisis of rights and interests, and contestations in Nigerian cities” (Onyebueke, et al., in press) [1]. This data article presents relevant national and city-level data on patterns, trend, and impacts of reported urban infrastructure-related displacements in Nigeria between 2010 and 2016. The data of reported cases of displacements in Nigeria is presented in tabular matrix with, on the one hand, nested rows designated as six Sub-regions, 36 States and Abuja Federal Capital Territory, and Case Location/Local Government Area, and, on the other, 14 columns of variables. The rows were itemized correspondingly as follows: Type of infrastructure project; Mode of displacement; Status of displacement; Mechanisms of displacement; Reasons given for displacement; Project funder/initiator; Type of displacement; Number of people/persons affected; Reported social characteristics of people affected; Response of those affected; Action(s) Outcome; Source(s) of information and link; Date(s) reported; and Notes. By synthesizing secondary information sources on the subject matter, it is hoped that this data will bridge the information gap and lay a solid foundation for a repository of existing and future information, while serving as a resource for advocacy at national and sub-national levels

The spreadsheet consists of multiple attributes of case study cities.

This is a data sheet that we use as part of Interpretative analysis of interviews that we had conducted with local residents and officials.

In this study: Supplementary Material 1 contains the questionnaire survey. Supplementary Material 2 contains the audit checklist. Supplementary Material 3 contains the themes emerged from interviews.

Discounted Cash Flow analysis of a proposed housing development on a suburban site outside Adelaide, South Australia.

All the raster layers used in the study "Monitoring and Modeling of Spatio-Temporal Urban Growth of Delhi, India using Cellular Automata and Geoinformatics" have been shared as GeoTIFF files. The Python script used in the study is also shared.

1. Data on the number of population in the largest cities in Poland and neighboring municipalities in the years 2002-2016 2. Data on the surface of a flat per person in the largest cities in Poland and neighboring municipalities in the years 2002-2016