Evaluating the Territorial Impact of Built-Up Area Expansion in the Surroundings of Bucharest (Romania) through a Multilevel Approach Based on Landsat Satellite Imagery

How Do the New Residential Areas in Bucharest Satisfy Population Demands, and Where Do They Fall Short?

In recent years, Bucharest’s residential dynamics have thrived, fueled by growing demand and an insufficient housing fund. This study aimed to analyze the residential satisfaction of those living in newly built dwellings. Its objectives were to identify the characteristics of three new residential areas and analyze the satisfaction level among residents regarding both their dwellings and neighborhoods. The investigation employed direct observations during the fieldwork phase (through observation sheets and mapping methods) and surveys (through questionnaires with residents and interviews with developers). Its results highlighted spaces that exhibit an increase in residential constructions, with a tendency to expand toward suburban areas, without necessarily meeting legislative requirements. When measuring the population’s residential satisfaction level, the study observed a general satisfaction regarding dwellings’ modernity and price but noticeable differences within the sample residential nuclei. The solutions proposed by residents mainly target authorities, who were held responsible for developing the urban infrastructure prior to granting building permits, as well as for vetting developers better and requiring them to respect the legislation. Hence, scientists, local authorities, real-estate developers and the local population represent the beneficiaries of the current study’s results.

Spillover Effects of Built-Up Land Expansion Under Ecological Security Constraint at Multiple Spatial Scales

Land-use change is a global issue, and the built-up land expansion has affected the ecological landscape patterns of the major river basins in the world. However, measurement of the ecological risks of potential landscape and identification of the dynamic relationships by natural and human-driven built-up land expansion at different zoning scales are still less understood. Based on multi-period Landsat satellite image data, we combined remote sensing (RS) and geography information systems (GIS) technologies with Spatial Durbin Panel Model to quantitatively analyze the landscape ecological effects under the built-up land expansion in the Yellow River Basin. The results showed that there is spatial heterogeneity in the built-up land expansion and ecological security patterns, with the expansion gravity center gradually spreading from the downstream to the middle and upstream areas, and the most dramatic change in landscape patches of ecological safety patterns occurring around the year 2000. At different zoning scales, there is a spatial spillover effect on the interaction between built-up land expansion and ecological security, with the significance of the regression estimates decreasing from large sample sizes to small sample sizes. Our findings highlighted the importance of spatial heterogeneity at different zoning scales in identifying the dynamic relationship between built-up land expansion and ecological security, scientific planning of land resources, and mitigation of ecological and environmental crises.

Mechanisms of Change in Urban Green Infrastructure—Evidence from Romania and Poland

The extent and continuity of green infrastructure can be adjusted by planning. Depending on the sense of the adjustment, the process can lead to a vicious cycle, resulting in poorer urban quality, or to a virtuous planning, thus leading to psychological wellbeing and sustainability. However, socioeconomic circumstances also play an important role in managing green infrastructure. Starting from these premises, the current study aims to take an in-depth look at the mechanisms of change in urban green infrastructure and provide concrete planning recommendations for dealing with the green infrastructure. It is based on a complex approach, combining an ecological design, including geo-statistical analyses of the structure and dynamics of different categories of green infrastructure in all Romanian and Polish cities covered by the Urban Atlas data during 2006–2018, with selected case studies for analyzing the deeper mechanisms and drivers of change in green infrastructure, and focusing on the role of different planning actors. The results indicate that green infrastructure was lost in all the cities analyzed, regardless of the different planning systems of the two countries. Based on this, specific recommendations can be phrased for all stakeholders of the planning process, including planners, local administrations, policy makers, and scientists.

Combining Object-Based Machine Learning with Long-Term Time-Series Analysis for Informal Settlement Identification

Informal settlement mapping is essential for planning, as well as resource and utility management. Developing efficient ways of determining the properties of informal settlements (when, where, and who) is critical for upgrading services and planning. Remote sensing data are increasingly used to understand built environments. In this study, we combine two sources of data, very-high-resolution imagery and time-series Landsat data, to identify and describe informal settlements. The indicators characterising informal settlements were grouped into four different spatial and temporal levels: environment, settlement, object and time. These indicators were then used in an object-based machine learning (ML) workflow to identify informal settlements. The proposed method had a 95% overall accuracy at mapping informal settlements. Among the spatial and temporal levels examined, the contribution of the settlement level indicators was most significant in the ML model, followed by the object-level indicators. Whilst the temporal level did not contribute greatly to the classification of informal settlements, it provided a way of understanding when the settlements were formed. The adaptation of this method would allow the combination of a wide-ranging and diverse group of indicators in a comprehensive ML framework.