Building population mapping with aerial imagery and GIS data

Ural, Serkan; Hussain, Ejaz; Shan, Jie

doi:10.1016/j.jag.2011.06.004

Cited by 94 publications

(62 citation statements)

References 20 publications

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“…Since the size of a building and its type provide a cue on the number of residents, using 3D geoinformation to estimate the population has been a topic of several research papers [74,[327][328][329][330][331][332][333][334][335][336][337][338][339].…”

Section: Estimating the Population In An Areamentioning

confidence: 99%

Applications of 3D City Models: State of the Art Review

Biljecki

Stoter

Ledoux

et al. 2015

IJGI

632

380

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In the last decades, 3D city models appear to have been predominantly used for visualisation; however, today they are being increasingly employed in a number of domains and for a large range of tasks beyond visualisation. In this paper, we seek to understand and document the state of the art regarding the utilisation of 3D city models across multiple domains based on a comprehensive literature study including hundreds of research papers, technical reports and online resources. A challenge in a study such as ours is that the ways in which 3D city models are used cannot be readily listed due to fuzziness, terminological ambiguity, unclear added-value of 3D geoinformation in some instances, and absence of technical information. To address this challenge, we delineate a hierarchical terminology (spatial operations, use cases, applications), and develop a theoretical reasoning to segment and categorise the diverse uses of 3D city models. Following this framework, we provide a list of identified use cases of 3D city models (with a description of each), and their applications. Our study demonstrates that 3D city models are employed in at least 29 use cases that are a part of more than 100 applications. The classified inventory could be useful for scientists as well as stakeholders in the geospatial industry, such as companies and national mapping agencies, as it may serve as a reference document to better position their operations, design product portfolios, and to better understand the market.

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Section: Estimating the Population In An Areamentioning

confidence: 99%

Applications of 3D City Models: State of the Art Review

Biljecki

Stoter

Ledoux

et al. 2015

IJGI

632

380

View full text Add to dashboard Cite

show abstract

“…In the past, classical information of this type was provided by topographic maps. Today, a more-important role is being played by remote sensing sources such as aerial and satellite imaging as well as the products of their processing created during the interpretation or classifi cation process -land use and land cover (LULC) maps [1,14]. The possibility of a dynamic estimation of the number of people based on the data gathered from mobile device users is also tested [5].…”

Section: Introductionmentioning

confidence: 99%

Distribution of Krakow’s population by dasymetric modeling method using Urban Atlas and publicly available statistical data

Pirowski

Pomietłowska

2017

geom

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Summary:This publication att empts to adapt the dasymetric method of mapping statistical areas to study population density and carry out the mapping of population distribution of the city of Krakow. Population studies of cities are an important issue, both from a spatial and urban planning point of view and in the preparation of market or crisis studies. Existing maps of the population of Krakow come down to simple cartograms referenced in administrative divisions. Based on the approaches given in literary sources (and modifying them appropriately with regard to the nature and detail of the data already in place), three population distribution maps of Krakow were developed using the binary method and -in two variable options -surface-weight aggregation. Arbitrarily adopted numbers of people assigned to particular types of land cover (the fi rst two implemented approaches) generalize the population distribution of the city and/or assume the possibility of a direct transfer of experience from other areas to the analyzed area. Therefore, in the third solution, an optimization of the surface-weight aggregation method was made, obtaining new characteristic proportions for Krakow in the population density for inhabited categories of land cover. When analyzing cartograms and histograms of errors noted within the urban units, the validity of this approach was confi rmed.The map generated provides a bett er refl ection of the actual spatial diversity of the population of Krakow than the previously used simple cartograms (or the dasymetric modeling, which did not take into account the specifi cs of the mapped area).

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“…Building footprint size is characteristic for extracting the number of apartments and determining the type of that building also crucial to extract information about the apartment number in a building and usage of a building. For instance, some parcels include residential utility buildings such as garages, sheds and barns (Ural et al, 2011) (Ural et al, 2011). The average population density for districts is calculated as 0.26 people/100 m 2 and for buildings is 3.49.…”

Section: Methodsmentioning

confidence: 99%

Exploring Scale Effect Using Geographically Weighted Regression on Mass Dataset of Urban Robbery

Yavuz

Tecim

2013

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Urban geographers have been studying to explain factors influencing crime on cases limited by their study areas. Researchers have a common opinion that explanatory variables modelling crime on those cases might be irrelevant for another one. None of the researchers tested significance of these variables with changing scales of the study area. Because their data did not allow them to study with different scales. This research examines the scale effect with various data from a wide range of data sources. Geographically Weighted Regression (GWR) method is used to explain that effect, after organizing data by Geographical Information System (GIS) technologies. Explanatory variables deduced for district scale are different from those for grid scale. Hence, the explanatory variables may change not only for different geographical areas but also for different scales of the same area.

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Building population mapping with aerial imagery and GIS data

Cited by 94 publications

References 20 publications

Applications of 3D City Models: State of the Art Review

Applications of 3D City Models: State of the Art Review

Distribution of Krakow’s population by dasymetric modeling method using Urban Atlas and publicly available statistical data

Exploring Scale Effect Using Geographically Weighted Regression on Mass Dataset of Urban Robbery

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