Four-Dimensional Map Reconstruction for Urban Progress Analysis

Abstract

The article describes a simple and low cost methodology of four-dimensional map creation, based on the main elements of the urban world like such as green ground, buildings, soil, water area and others, which makes it possible to detect the urban progress during a certain period using an open source data of Google Earth and geographical information system. This implies that a study of changes in urban elementrequire an integration of spatial information and corresponding real time, which is referred to as the four-dimensional map. Accordingly, the fourth dimensional (time) was added to the three dimensional spatial information (3 dimensional) study area signifies by the University of Baghdad, Aljadrya Campus. Regarding the article, the study area’s urban progress was considered for Google Earth’s available period of the data set that started from 2002 to 2019 at each of 2002, 2005, 2010 in addition to 2019 before being updated from the field observation. The main goal of this article is to provide an important indicator that can be used to determine the nature of current growth and forecast it in the future. Furthermore, it can be used for solving the problems of negative urban progress, which is what most developing countries are experiencing. Findings show significant changes in the main objects of the study area are represented by increasing each of buildings, green grounds, play grounds by about 40.9%, 65.4% and 30.2% respectively, which are offset by decrease insoil grounds of nearly 20.8%.

Towards a spatio‐temporal multicriteria evaluation method: A suitability analysis of residential units in a 3D urban environment

“…Many spatio‐temporal modeling studies that address the 3D component of the urban fabric have focused primarily on reconstruction and visualization of the evolution of historic urban areas within a navigable 3D environment (Farella et al, 2021; Kaňuk et al, 2015; Takase et al, 2008). However, spatio‐temporal city models have also been applied to simulate and visualize historic urban flood inundation (Seenu et al, 2020), analyze change in urban material metabolism (Schandl et al, 2020; Tanikawa & Hashimoto, 2009), and quantify changes in percent cover of various urban elements (Hussein et al, 2020). Furthermore, studies such as Jaillot et al (2021) have proposed advanced frameworks for web‐based spatio‐temporal city models that can also integrate multimedia documents for enriched information sharing.…”