Quantitative Comparison of Open-Source Data for Fine-Grain Mapping of Land Use

Deng, Xueqing; Newsam, Shawn

doi:10.1145/3152178.3152182

Cited by 10 publications

(9 citation statements)

References 18 publications

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“…Nowadays, the great data collecting potential of crowd-sourced social media is being exploited to analyze a diverse range of topics related to the functional organization of the city (Arribas-Bel and Tranos, 2018), such as the relationship between urban form and function (Crooks, Pfoser et al, 2015;Crooks, Croitoru et al, 2016); the identification of POIs-points of interest- (Van Canneyt et al, 2012;Deng and Newsam, 2017;García-Palomares et al, 2015;Van Weerdenburg et al, 2019) and their accessibility in terms of density and diversity (Shen and Karimi, 2016); the characterization of livelihoods according to the collective behaviors of residents (Cranshaw et al, 2012); and the delimitation of functional areas to understand social and spatio-temporal aspects of the city (Chen et al, 2017;Rösler and Liebig, 2013).…”

Section: Urban Activity Patterns Through Location-based Social Networkmentioning

confidence: 99%

“…Specifically, this classification is the one that addresses the "functional dimension," one of the five available LBCS dimensions, which refers to "the economic function or type of establishment using the land" (American Planning Association, 2018b). This hierarchical classification provides an overall fine-grain land use class taxonomy of nine Level 1 categories, 47 Level 2 categories, and 159 Level 3 categories (Deng and Newsam, 2017). All refined Google Places place types were assigned to APA Level 1 (See Figure 2) and Level 2 (See Table 1) category codes, respectively.…”

Section: Recategorizing Operating Datapoints: From Google Places Categories To Apa Land Based Classification Standards Benchmark Categorimentioning

confidence: 99%

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Revisiting the Spatial Definition of Neighborhood Boundaries: Functional Clusters versus Administrative Neighborhoods

Ciriquián

Serrano-Estrada

Nolasco‐Cirugeda

et al. 2021

Journal of Urban Technology

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This study revisits the debate surrounding the definition of neighborhood boundaries by addressing the disconnect between the city's Administrative Neighborhoods and its functional organization. A method is proposed for dividing the city into more meaningful units through the spatial distribution of urban activities by retrieving data from Google Places. The dataset was pre-processed and spatially divided into Functional Clusters. A comparison between functional and administrative subdivisions of the city was undertaken, from which three overall conclusions could be drawn. First, a function-based city partition allows economically active urban areas to become the neighborhood's center, thereby creating a polynuclear neighborhood structure that would potentially encourage greater cross-movement of people throughout the city. Second, the specialization of activities becomes more evident in Functional Clusters than in Administrative Neighborhoods. Third, access to up-to-date data makes possible a timely diagnosis of the quantity and diversity of urban activities-i.e., economic activities, services, and facilitiesthrough Google Places data. The value of this contribution is to inform urban decision-making and policies in order to better balance the provision of a neighborhood's economic activity.

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Section: Urban Activity Patterns Through Location-based Social Networkmentioning

confidence: 99%

Section: Recategorizing Operating Datapoints: From Google Places Categories To Apa Land Based Classification Standards Benchmark Categorimentioning

confidence: 99%

Revisiting the Spatial Definition of Neighborhood Boundaries: Functional Clusters versus Administrative Neighborhoods

Ciriquián

Serrano-Estrada

Nolasco‐Cirugeda

et al. 2021

Journal of Urban Technology

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“…And this method is heavily depended on the availability of photos that has geo-information. Besides ground-level images, some researches are using point of interest information from social network which contains the functional and locational properties (Ty et al, 2016, Deng, Newsam, 2017. Even these methods work well in cities, it is still an approximate estimate.…”

Section: Related Workmentioning

confidence: 99%

Efficient Building Category Classification With Façade Information From Oblique Aerial Images

Xiao

Xie

Zhang

et al. 2020

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

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Abstract. Building category refereed to categorizing structures based on their usage is useful for urban design and management and can provide indexes of population, resource and environment related problems. Currently, the statistics are mainly collected by manual from street data or roughly extracted from remote sensing data which are either laborious or too coarse. With remote sensing data (e.g. satellite and aerial images), buildings can be automatically identified from the top-view, but the detailed categories of single buildings are not recognized. Façade from oblique-view image can greatly help us to identify the categories of buildings, for example, balcony usually exist in resident buildings. Hence, in this paper, we propose an efficient way to classify building categories with the façade information. Firstly, following the texture mapping procedure, each building’s façade textures are cropped from oblique images via a perspective transformation. Then, the average colour, the stander deviation in R, G, B channel, and the rectangle Haar-like features are extracted and feed to a further random forest classifier for their category identifications. In the experiment, we manually selected 262 building façades that can be classified into four functional types as: 1) regular residence ; 2) educational building; 3) office ; 4) condominium. The results shows that, with 30% data as training samples, the classification accuracy can reach 0.6 which is promising in real applications and we believe with more sophisticated feature descriptors and classifiers, e.g., neuronal networks, the accuracy can be much higher.

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“…En cualquier caso, ambas redes sociales recopilan, de uno u otro modo, una base de datos referente tanto a la oferta de actividades económicas como a su demanda -de acuerdo con sus registros en forma de places o venues-puesto que ambas cuentan con una variable que recoge la valoración cuantitativa que los usuarios hacen de la actividad -rating-. Concretamente, la información contenida en Google Places permite observar de qué manera las actividades comerciales y empresariales se complementan con otras como la restauración u otros tipos de ocio (Deng y Newsam, 2017;Milne, Thomas y Paris, 2012;Van Canneyt et al, 2012); y, Foursquare permite analizar cuáles son las actividades más atractivas en un determinado contexto urbano (Noulas, Mascolo y Frias-Martinez, 2013;Bentley, Cramer y Müller, 2015). Además, a través de datos de otras redes sociales como Airbnb es posible estudiar también la oferta y la demanda en el alojamiento temporal no regulado, así como cuáles son los tipos de alquiler -total o parcial-y los tipos de inmuebles -apartamentos, viviendas aisladas, etc.-más demandados por este tipo de actividad en distintos ámbitos y ciudades (Perez-Sanchez et al, 2018;Moreno-Izquierdo et al, 2019).…”

Section: Estudio De La Oferta Y Demanda De La Actividad Económicaunclassified

Los datos geolocalizados de las redes sociales en el estudio de cuestiones urbanas complejas: cinco temas, cinco redes

Ciriquián¹,

Nolasco‐Cirugeda²,

Serrano-Estrada³

2019

ACE: Architecture, City and Environment

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Geolocated data generated by social networks allow various kinds of urban-related issues to be addressed, broadening knowledge on the different dynamics that occur in the city. This study has a twofold objective: (1) to identify which data variables from five well known social networks-Foursquare, Instagram, Twitter, Google Places and Airbnb-offer potential insights on the study of specific urban phenomena; and, (2) to delve into five exemplary urban topics that can be tackled by analysing the data from these social networks. Methodology Social media are analysed and specific urban issues that can be assessed through their data variables are identified. Specifically, five themes that encompass several research issues are identified through the combination of different data variables. Conclusions Data generated in social networks used for urban study purposes provide a valuable opportunity to address issues such as: [1] the use and preference for certain activities and places; [2] the image and perception of urban spaces; [3] spatiotemporal patterns of people presence and urban space-use; [4] supply and demand of economic activity; and, [5] identification of areas of concentration and specialization in the city. Originality This up to date and easily accessible information provides a thorough picture of complex issues in relation to urban dynamics that, until recently, were not easily measurable and quantifiable. Thus, the interpretation of these data broadens knowledge on city dynamics that can be useful for decision-making processes in the planning and design of the city.

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Quantitative Comparison of Open-Source Data for Fine-Grain Mapping of Land Use

Cited by 10 publications

References 18 publications

Revisiting the Spatial Definition of Neighborhood Boundaries: Functional Clusters versus Administrative Neighborhoods

Revisiting the Spatial Definition of Neighborhood Boundaries: Functional Clusters versus Administrative Neighborhoods

Efficient Building Category Classification With Façade Information From Oblique Aerial Images

Los datos geolocalizados de las redes sociales en el estudio de cuestiones urbanas complejas: cinco temas, cinco redes

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