How to Describe Basic Urban Pattern in Geographic Information Systems

Germanaite, Indraja; Butleris, Rimantas; Zaleckis, Kęstutis

doi:10.1007/978-3-319-99972-2_12

Cited by 5 publications

(13 citation statements)

References 18 publications

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“…Spatial Pattern Description and Taxonomy SP depicts a complex physical entity or any kind of structure, spatial distribution, or recurring feature that is represented as lines, areas, or bodies in a 2D or 3D map and can be described by pattern specification [Marshall and Gong 2009;Germanaite et al 2018]. The identification of SP types, properties and measures can be performed by using urban geometry, topology, morphology and transformation.…”

Section: 1mentioning

confidence: 99%

“…Some SP such as Alexander patterns currently can be detected only by empirical observation, though they also should be referred as SP. Despite this, there are many qualitative (morphological and morphographic descriptions) and quantitative (network and fractal analysis) methods to identify SP [Marshall and Gong 2009;Germanaite et al 2018].…”

Section: Spatial Pattern Identification Techniquesmentioning

confidence: 99%

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Case Study of Spatial Pattern Description, Identification and Application Methodology

Germanaite¹,

Zaleckis²,

Butleris³

et al. 2020

jucs

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In this case study the authors created and tested a configurable and expandable spatial patterns (SP) description, identification, and application methodology (SPDIAM) and an SP identification algorithm. SPDIAM allows urban planning and design (UPD) practitioners to describe SP in a computerized manner, identify SP automatically and then apply them in the UPD domain. SPDIAM is based on the space syntax (SS) method and normalized spatial and non-spatial measures and can be used with the statistical social, economic, and environmental indicators, which are related to the urban sustainability and spatial capital. The goal of the case study experiment was to proof a concept of SPDIAM and to identify the rules and the values of the measures used for the SP identification. For this City Layout SP was identified in the vector data of 12 European, North American, and African cities. The experiment results confirmed that SPDIAM is appropriate to describe SP and identify them automatically. The use of the normalized measures enables the comparison of different SP and reduces the degree of the subjectivity of the UPD solutions. SPDIAM no longer relies on statistical information but forms SP based on the probabilistic complex modelling of a city, which lets SPDIAM indicate possible directions of SP future transformation. SPDIAM uses the newly offered measures CENTER and URBAN COMPACTNESS INDEX to identify SP automatically and can add quantitative and qualitative improvement to the spatial network analysis tools in Geographic Information Systems.

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Section: 1mentioning

confidence: 99%

Section: Spatial Pattern Identification Techniquesmentioning

confidence: 99%

Case Study of Spatial Pattern Description, Identification and Application Methodology

Germanaite¹,

Zaleckis²,

Butleris³

et al. 2020

jucs

Self Cite

View full text Add to dashboard Cite

show abstract

“…Though SP can be regarded as abstract types that allow generalization and a better understanding of the spatial entities, there is no single correct way of classifying patterns or identifying pattern types [Marshall and Gong 2009;Germanaite et al 2018]. The primary and the most comprehensive source of SP is Alexander [Alexander 1977] pattern language that describes ~250 patterns of the towns, buildings and structures.…”

Section: 1mentioning

confidence: 99%

“…In the methodology section, the static and dynamic views of SPDIAM using UML diagrams and spatial data preparation routine are discussed. For this purpose, a basic urban pattern UML class and data model [Germanaite et al 2018] were extended and a description of the SP identification method was added. SS method was selected to detect SP in spatial data as it provides a set of theories and methods for the analysis of spatial configurations of all kinds and at all scales [Germanaite et al 2018].…”

Section: Introductionmentioning

confidence: 99%

“…For this purpose, a basic urban pattern UML class and data model [Germanaite et al 2018] were extended and a description of the SP identification method was added. SS method was selected to detect SP in spatial data as it provides a set of theories and methods for the analysis of spatial configurations of all kinds and at all scales [Germanaite et al 2018]. In the experiment section, the City Layout SP is described using the proposed UML class model, SS method and measures, and the value of the City Layout SP is identified in the vector data of 12 European, North American and African cities.…”

Section: Introductionmentioning

confidence: 99%

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Undersampling Instance Selection for Hybrid and Incomplete Imbalanced Data

Camacho-Nieto

Yáñez-Márquez²,

Villuendas-Rey

2020

jucs

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The challenges of integrating explainable artificial intelligence into GeoAI

Jin

Sieber

2023

Transactions in GIS

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Although explainable artificial intelligence (XAI) promises considerable progress in glassboxing deep learning models, there are challenges in applying XAI to geospatial artificial intelligence (GeoAI), specifically geospatial deep neural networks (DNNs). We summarize these as three major challenges, related generally to XAI computation, to GeoAI and geographic data handling, and to geosocial issues. XAI computation includes the difficulty of selecting reference data/models and the shortcomings of attributing explanatory power to gradients, as well as the difficulty in accommodating geographic scale, geovisualization, and underlying geographic data structures. Geosocial challenges encompass the limitations of knowledge scope—semantics and ontologies—in the explanation of GeoAI as well as the lack of integrating non‐technical aspects in XAI, including processes that are not amenable to XAI. We illustrate these issues with a land use classification case study.

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How to Describe Basic Urban Pattern in Geographic Information Systems

Cited by 5 publications

References 18 publications

Case Study of Spatial Pattern Description, Identification and Application Methodology

Case Study of Spatial Pattern Description, Identification and Application Methodology

Undersampling Instance Selection for Hybrid and Incomplete Imbalanced Data

The challenges of integrating explainable artificial intelligence into GeoAI

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