Land-use structure, urban growth, and periurban landscape: a multivariate classification of the European cities

The aim of investigation was to discover territorial differences in landscape technosphere (structural imprint from cultural and technological processes). The main processes forming technosphere are related to technology‐bound areal changes that transform landscape through land use change and urbanization (the latter term taken in ‘building up’ sense). This specific layer can be divided horizontally into areas (1969 individual ones, in the Lithuanian case, averagely 33 km2 in size each) with a homogeneous land‐use structure and organically unbreakable techno‐structural elements (settlements with a road net), these areas being named techno‐morpho‐topes (TMTs). Complex classification of TMTs according to the largest urbocomplex (settlements and other built‐up complexes) inside them and dominating land‐use gave 10 types of areal technogenization that were mapped. Analysis of classified TMT mosaic allowed to distinguish 53 individual technosphere regions having a homogeneous, irregular or rhythmic mosaic texture of TMT types. Asymmetry of regions in regard to large cities seems to be a dominating rule (the largest city is usually in the peripheral part of the region). The obtained data could be applied in land management by finding the best way to relate the administrative and economic regional system and technosphere structure. Relations of the technosphere regions with relief was traced in few places. This implies further investigations on the subject of relations between landscape natural and cultural elements, structures and processes. Santrauka Tyrimo tikslas buvo atskleisti kraštovaizdžio technosferos (kultūrinių procesų struktūrinio įspaudo) teritorinius skirtumus Lietuvoje. Pagrindiniai kultūrinimo procesai susiję su plotiniais pokyčiais. Kraštovaizdžio transformavimasis vyksta dėl žemėnaudos pokyčių ir urbanizacijos (užstatymo plėtros prasme), įgyvendinamų technologijų. Sukultūrinimas lemia ypatingą sluoksnį ant gamtinių kraštovaizdžio komponentų. Horizontaliai šis sluoksnis gali būti suskaidytas į nedidelius plotus (Lietuvoje – 1969 vidutinio 33 km2 ploto individualius arealus), kuriuose homogeniška žemėnaudos struktūra ir organiškai vientisi technostruktūriniai elementai (gyvenvietės ir kelių tinklas). Šie teritoriniai vienetai pavadinti technomorfotopais (TMT). Remiantis kompleksine TMT klasifikacija, pagal didžiausią jų viduje esantį urbokompleksą (gyvenvietė arba kitas užstatytas plotas) ir vyraujančią naudmeną nustatyta 10 plotinės technogenizacijos tipų. Jie buvo kartografuoti. Analizuojant klasifikuotų TMT mozaikos žemėlapį išskirti 53 individualūs technosferos regionai. Vienų jų TMT plotinės technogenizacijos tipų mozaika, jos tekstūra vienalytė, kitų netvarkinga, trečių ritmiška. Paplitęs bruožas – daugelio regionų asimetrija didžiausio jiems priklausančio miesto atžvilgiu (didžiausias miestas paprastai yra regiono periferijoje, paribyje). Gauti duomenys gali būti pritaikyti kraštotvarkoje geriausiai sąsajai tarp administracinių bei ekonominių regionų ir technosferos struktūros rasti. Ryšys tarp technosferos regionų ir reljefo bruožų pastebimas tik dalyje Lietuvos teritorijos. Ryšiams tarp kraštovaizdžio gamtinių ir kultūrinių elementų, struktūrų ir procesų atskleisti būtini išsamesni tyrimai. Резюме Целью исследования было выявление территориальной неравномерности техносферы (структурного отпечатка культурных процессов) ландшафта в Литве. Главные процессы окультуривания связаны с площадными изменениями, которые осуществляются с помощью технических приспособлений, трансформирующих ландшафт через изменения земельных угодий и урбанизацию (распpостранения застроенных площадей). Слой техносферы, как бы надетый на природные компоненты ландшафта, горизонтально может быть расчленен на небольшие ареалы (в Литве – 1969 единиц со средней площадью 33 км2 ) с однородной структурой землеугодий и органически целостными техно-структурными элементами (населенными или другими застроенными местностями с дорожной сетью). Эти территориальные единицы названы техноморфотопами (ТМТ). Комплексная классификация ТМТ по двум признакам – доминирующему по площади типу застроенной территории (урбокомплекса) и доминирующих землеугодий – дала 10 типов площадной (ареальной) техногенизации, что было картографировано. Анализ типовой мозаики позволил выявить 53 индивидуальных района окультуривания с разными текстурными типами мозаики: однородным, беспорядочным или ритмичным. Наблюдается закономерная асимметрия многих районов, заключающих в себе большие и средние города: самая большая населенная местность находится на окраине, почти примыкающей к границе района. Полученные данные могут быть использованы в краеустройстве при нахождении оптимальной взаимосвязи между административной, а также экономической территориальными структурами и регионами техносферы. Территориальная связь между районами техносферы и рельефом наблюдается лишь местами. Поэтому необходимы более детальные исследования для выявления связей между природными и культурными элементами, структурами и процессами.

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Machine Learning Algorithms for Urban Land Use Planning: A Review

Urban Science ◽

10.3390/urbansci5030068 ◽

2021 ◽

Vol 5 (3) ◽

pp. 68

Author(s):

Vineet Chaturvedi ◽

Walter T. de Vries

Keyword(s):

Machine Learning ◽

Land Use ◽

Land Use Change ◽

Urban Growth ◽

Land Use Planning ◽

Pattern Analysis ◽

Urban Land ◽

Earth Observation ◽

Urban Land Use

Urbanization is persistent globally and has increasingly significant spatial and environmental consequences. It is especially challenging in developing countries due to the increasing pressure on the limited resources, and damage to the bio-physical environment. Traditional analytical methods of studying the urban land use dynamics associated with urbanization are static and tend to rely on top-down approaches, such as linear and mathematical modeling. These traditional approaches do not capture the nonlinear properties of land use change. New technologies, such as artificial intelligence (AI) and machine learning (ML) have made it possible to model and predict the nonlinear aspects of urban land dynamics. AI and ML are programmed to recognize patterns and carry out predictions, decision making and perform operations with speed and accuracy. Classification, analysis and modeling using earth observation-based data forms the basis for the geospatial support for land use planning. In the process of achieving higher accuracies in the classification of spatial data, ML algorithms are being developed and being improved to enhance the decision-making process. The purpose of the research is to bring out the various ML algorithms and statistical models that have been applied to study aspects of land use planning using earth observation-based data (EO). It intends to review their performance, functional requirements, interoperability requirements and for which research problems can they be applied best. The literature review revealed that random forest (RF), deep learning like convolutional neural network (CNN) and support vector machine (SVM) algorithms are best suited for classification and pattern analysis of earth observation-based data. GANs (generative adversarial networks) have been used to simulate urban patterns. Algorithms like cellular automata, spatial logistic regression and agent-based modeling have been used for studying urban growth, land use change and settlement pattern analysis. Most of the papers reviewed applied ML algorithms for classification of EO data and to study urban growth and land use change. It is observed that hybrid approaches have better performance in terms of accuracies, efficiency and computational cost.

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ECOLOGICAL AND ECONOMIC EFFECTIVENESS OF USE LAND IN CONDITIONS OF INFRINGEMENT OF INSTITUTIONAL TRANSFORMATIONS

Environmental Economics and Sustainable Development ◽

10.37100/2616-7689/2018/1-2(21-22)/11 ◽

2018 ◽

pp. 58-61

Author(s):

Oksana Sakal

Keyword(s):

Land Use ◽

Social Welfare ◽

Agricultural Land ◽

Economic Value ◽

Economic Effectiveness ◽

Land User ◽

Total Economic Value ◽

Definition Of ◽

Welfare Theory

The article is devoted doctrinal issues of environmental and economic effectiveness of use land in conditions of infringement of institutional transformations. The modern approaches to the definition of content of ecological and economic effectiveness of land use are analyzed. It is established that the overwhelming majority of domestic researchers interpret this notion regarding the use of agricultural land or farm land. It is proved that such an approach is justified, taking into account the structure of the land fund of Ukraine. However, this reduces other goals of the land user and functions of the land. It is proposed to investigate the category of ecological and economic effectiveness of land use in accordance with the provisions of the ecological economics, social welfare theory, and concept of total economic value. Based on the classification of land functions, the criteria of selection material content and social form of ecological and economic effectiveness of land use are determined.

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Methodological aspects of the spatial analysis of the land use structure in the Kasimov district at the end of the XVIII century

Geodesy and Cartography ◽

10.22389/0016-7126-2016-909-3-56-61 ◽

2016 ◽

Vol 909 (3) ◽

pp. 56-61 ◽

Cited By ~ 1

Author(s):

V.M. Matasov ◽

Keyword(s):

Land Use ◽

Spatial Analysis ◽

Xviii Century ◽

Land Use Structure ◽

Methodological Aspects

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Urban Land-Use and Land-Cover Mapping Based on the Classification of Transport Demand and Remote Sensing Data

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss39084.2020.9323497 ◽

2020 ◽

Author(s):

Chiara Tacconi ◽

Maria Pia Tuscano ◽

Gabriele Moser ◽

Nicola Sacco

Keyword(s):

Remote Sensing ◽

Land Use ◽

Land Cover ◽

Remote Sensing Data ◽

Urban Land ◽

Urban Land Use ◽

Land Cover Mapping ◽

Sensing Data

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Optimization of a Novel Urban Growth Simulation Model Integrating an Artificial Fish Swarm Algorithm and Cellular Automata for a Smart City

Sustainability ◽

10.3390/su13042338 ◽

2021 ◽

Vol 13 (4) ◽

pp. 2338

Author(s):

Xinxin Huang ◽

Gang Xu ◽

Fengtao Xiao

Keyword(s):

Land Use ◽

Sustainable Development ◽

Urban Growth ◽

Smart City ◽

Urban Land ◽

Urban Land Use ◽

Growth Simulation ◽

Artificial Fish Swarm Algorithm ◽

Artificial Fish Swarm ◽

Ca Model

As one of the 17 Sustainable Development Goals, it is sensible to analysis historical urban land use characteristics and project the potentials of urban sustainable development for a smart city. The cellular automaton (CA) model is the widely applied in simulating urban growth, but the optimum parameters of variables driving urban growth in the model remains to be continued to improve. We propose a novel model integrating an artificial fish swarm algorithm (AFSA) and CA for optimizing parameters of variables in the urban growth model and make a comparison between AFSA-CA and other five models, which is used to study a 40-year urban land growth of Wuhan. We found that the urban growth types from 1995 to 2015 appeared relatively consistent, mainly including infilling, edge-expansion and distant-leap types in Wuhan, which a certain range of urban land growth on the periphery of the central area. Additionally, although the genetic algorithms (GA)-CA model and the AFSA-CA model among the six models due to the distance variables, the parameter value of the GA-CA model is −15.5409 according to the fact that the population (POP) variable should be positively. As a result, the AFSA-CA model regardless of the initial parameter setting is superior to the GA-CA model and the GA-CA model is superior to all the other models. Finally, it is projected that the potentials of urban growth in Wuhan for 2025 and 2035 under three scenarios (natural urban land growth without any restrictions (NULG), sustainable urban land growth with cropland protection and ecological security (SULG), and economic urban land growth with sustainable development and economic development in the core area (EULG)) focus mainly on existing urban land and some new town centers based on AFSA-CA urban growth simulation model. An increasingly precise simulation can determine the potential increase area and quantity of urban land, providing a basis to judge the layout of urban land use for urban planners.

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