2020
DOI: 10.1016/j.cities.2020.102745
|View full text |Cite
|
Sign up to set email alerts
|

Spatiotemporal dynamics and the contributing factors of residential vacancy at a fine scale: A perspective from municipal water consumption

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1

Citation Types

0
4
0

Year Published

2021
2021
2024
2024

Publication Types

Select...
9

Relationship

0
9

Authors

Journals

citations
Cited by 21 publications
(4 citation statements)
references
References 55 publications
0
4
0
Order By: Relevance
“…The high concentric vacancy in the city center of Sapporo corresponds to the cases of Changshu, China [94] and Sao Paulo, Brazil [95]. In both these cases, the decline in the central cities and out-migration accounted for the rise in the number of vacancies.…”
Section: Spatial Distribution Of the Vacant Housesmentioning
confidence: 95%
“…The high concentric vacancy in the city center of Sapporo corresponds to the cases of Changshu, China [94] and Sao Paulo, Brazil [95]. In both these cases, the decline in the central cities and out-migration accounted for the rise in the number of vacancies.…”
Section: Spatial Distribution Of the Vacant Housesmentioning
confidence: 95%
“…Both of these research directions represented temporal patterns as a single point in time, as virtually no geosocial media check-in platforms record visits as durations. This practice of using point-in-time-based temporal patterns has seen numerous contributions in recent years examining everything from residential vacancy rates [45] and criminal activity [47], to the temporal and environmental factors contributing to park visits [20].…”
Section: Related Workmentioning
confidence: 99%
“…In recent decades, efforts have been invested in the investigation and monitoring of Residential Demand (DNR) of various water distribution networks (Cominola, Giuliani, Piga, Castelletti & Rizzoli, 2015), where valuable information is obtained for users. hydraulic models, as well as to generate synthetic DNR data of the systems, using various methodologies such as those used in Koutiva & Makropoulos (2016); Mostafavi, Gándara & Hoque (2018); Mostafavi, Shojaei, Beheshtian & Hoque (2018); and in Pan et al (2020).…”
Section: Introductionmentioning
confidence: 99%