Land Use Classification with Sparse Models

Ambient air pollution and its exposure has been a worldwide issue and can increase the possibility of health risks especially in urban areas of developing countries having the mixture of different air pollution sources. With the increase in population, industrial development and economic prosperity, air pollution is one of the biggest concerns in Pakistan after the occurrence of recent smog episodes. The purpose of this study was to develop a land use regression (LUR) model to provide a better understanding of air exposure and to depict the spatial patterns of air pollutants within the city. Land use regression model was developed for Lahore city, Pakistan using the average seasonal concentration of NO2 and considering 22 potential predictor variables including road network, land use classification and local specific variable. Adjusted explained variance of the LUR models was highest for post-monsoon (77%), followed by monsoon (71%) and was lowest for pre-monsoon (70%). This is the first study conducted in Pakistan to explore the applicability of LUR model and hence will offer the application in other cities. The results of this study would also provide help in promoting epidemiological research in future.

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A Comparative Analysis of Spatial Data and Land Use/Land Cover Classification in Urbanized Areas and Areas Subjected to Anthropogenic Pressure for the Example of Poland

Sustainability ◽

10.3390/su13063070 ◽

2021 ◽

Vol 13 (6) ◽

pp. 3070

Author(s):

Patrycja Szarek-Iwaniuk

Keyword(s):

Land Use ◽

Land Cover ◽

Land Cover Classification ◽

Classification Systems ◽

Anthropogenic Pressure ◽

Land Use Land Cover ◽

Land Use Classification ◽

Land Cover Data ◽

Spatial Changes ◽

Land Use Types

Urbanization processes are some of the key drivers of spatial changes which shape and influence land use and land cover. The aim of sustainable land use policies is to preserve and manage existing resources for present and future generations. Increasing access to information about land use and land cover has led to the emergence of new sources of data and various classification systems for evaluating land use and spatial changes. A single globally recognized land use classification system has not been developed to date, and various sources of land-use/land-cover data exist around the world. As a result, data from different systems may be difficult to interpret and evaluate in comparative analyses. The aims of this study were to compare land-use/land-cover data and selected land use classification systems, and to determine the influence of selected classification systems and spatial datasets on analyses of land-use structure in the examined area. The results of the study provide information about the existing land-use/land-cover databases, revealing that spatial databases and land use and land cover classification systems contain many equivalent land-use types, but also differ in various respects, such as the level of detail, data validity, availability, number of land-use types, and the applied nomenclature.

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A Cycle Gan Approach for Heterogeneous Domain Adaptation in Land Use Classification

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

10.1109/igarss39084.2020.9324264 ◽

2020 ◽

Author(s):

Claire Voreiter ◽

Jean-Christophe Burnel ◽

Pierre Lassalle ◽

Marc Spigai ◽

Romain Hugues ◽

...

Keyword(s):

Land Use ◽

Domain Adaptation ◽

Land Use Classification

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Neighbourhood places, collective efficacy and crime: A longitudinal perspective

Urban Studies ◽

10.1177/00420980211008820 ◽

2021 ◽

pp. 004209802110088

Author(s):

Renee Zahnow ◽

Jonathan Corcoran ◽

Anthony Kimpton ◽

Rebecca Wickes

Keyword(s):

Land Use ◽

Social Control ◽

Social Interactions ◽

Collective Efficacy ◽

Social Cohesion ◽

Mixed Effects ◽

Land Use Classification ◽

Social Survey ◽

Panel Models ◽

Over Time

Neighbourhood places like shops, cafes and parks support a variety of social interactions ranging from the ephemeral to the intimate. Repeated interactions at neighbourhood places over time lay the foundation for the development of social cohesion and collective efficacy. In this study, we examine the proposition that changes in the presence or arrangement of neighbourhood places can destabilise social cohesion and collective efficacy, which has implications for crime. Using spatially integrated crime, social survey and parcel-level land-use classification data, we estimate mixed effects panel models predicting changes in theft and nuisance crimes across 147 Australian neighbourhoods. The findings are consistent with neighbourhood social control and crime opportunity theories. Neighbourhood development – indicated by fewer vacant properties and fewer industrial and agricultural sites – is associated with higher collective efficacy and less crime over time. Conversely, introducing more restaurants, transit stations and cinemas is associated with higher theft and nuisance over time regardless of neighbourhood collective efficacy. We argue that the addition of socially conducive places can leave neighbourhoods vulnerable to crime until new patterns of sociability emerge and collective efficacy develops.

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