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Temporal dynamics of green urban areas in Romania. A comparison between spatial and statistical data

  • Andra-Cosmina Albulescu EMAIL logo and Daniela Larion
Published/Copyright: December 19, 2023
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Open Geosciences
From the journal Open Geosciences Volume 15 Issue 1

Abstract

The social function and the ecosystem services provided by green urban areas (GUAs) have long been acknowledged by both the scientific community and the public. They become even more relevant to urban sustainability and human welfare in the post-pandemic world, which still has to confront social, environmental, and energy-related issues. This study aims to provide an example of how to perform a temporal dynamics-focused analysis of GUAs in an unsatisfactory data context by comparing the results obtained via spatial and statistical datasets of 35 cities in Romania considering the 2006–2018 period as a reference. This integration represents a compromise solution that should offer insights into the GUA’s dynamics in difficult monitoring conditions determined by the absence of both high-resolution spatial datasets and metadata-enhanced statistical datasets. Specific patterns of the GUA’s dynamics are identified, and the differences between the spatial data and statistical data-based findings are discussed. For at least 78% of the analysed cities, the official statistical data show that the GUAs are larger than the ones computed via GIS, in all reference periods. The findings call for the improvement of the GUA quantification and mapping regulations and programmes, which target, among other elements, their transparency and validation.

Keywords: urban green infrastructure; green urban spaces; Urban dynamics; spatial analysis; Romania

1 Introduction

Urban settlements leave a distinct imprint on the environment, as more than half of the global population live in cities [1], and they continue to expand at the expense of natural and semi-natural environments. Within these “social-ecological systems,” [2] green areas may be considered islands where people reconnect with nature. Their social functions and the ecosystem services they provide become even more important nowadays when the world is recovering from the Covid-19 pandemic and still has to face new environmental and energy-related issues.

Green urban areas (GUAs) represent only a component of the green infrastructure (GI) of the cities, a concept that covers many definitions [3,4,5,6], although its development commenced only 20–30 years ago [7]. The definitions converge to the inclusion of the natural and semi-natural elements (open spaces, vegetation, and water features) that provide ecosystem services in both rural and urban environments. While some definitions highlight the network structure of the GI [8,9], others offer a hybrid interpretation by integrating the technical systems that regulate air, water, energy, sanitation, and pollution [10]. Also, it should be highlighted that there is a wide range of terms used in research works focusing on green areas located in urban settlements: GI, urban green infrastructure (UGI), GUAs, and urban green spaces (UGS). In this article, the terms GUA and UGS are interchangeably used, the first one being preferred, as it is also specific to the spatial dataset. GUAs are major parts of the UGI, which is also part of the broader GI concept. Benedict and McMahon [8] delimitate the two hierarchic notions by stating that green space is a self-sustaining urban functional unit that is viewed as “something that is nice to have,” while GI is a vital component of the urban system (“something that we must have”) that needs to be protected and managed.

Considering modern-time concerns, Cheshmehzangi et al. [11] argue that research on GI needs to revise the definitions and expand them to relate to public health, climate modifications, emissions, and energy issues.

GI substantially contributes to the sustainability of urban environments [3,4,7,11,12,13], particularly through their green components [14], which are largely responsible for the provision of ecosystem services. GUAs have a cooling effect, forming cool islands – provided they are large enough [15], offset carbon emissions [16], allow increased water infiltration [17], and shelter wildlife [8,18]. Some GUAs have significant tourism and recreation potential [19], also helping to avoid additional costs through their positive effects on human health [20]. Studies show that GUAs positively contribute to longevity [21], mental health, physical activity [22], stress reduction [23], and post-surgery recovery [24].

All the ecosystem services provided by the green areas may be impaired or lost, as the fine balance between the natural, semi-natural, and artificial areas within a city is disturbed, and the “odds” coordinated by the economic growth target favour the latter. Turning the urban green to grey constitutes a common practice, motivated on the grounds of densification, considered by some a key element related to sustainability [25,26,27].

In order to avoid ecosystem services loss, urban planning should prioritize green–grey integration [19] and thoroughly monitor the dynamics of this urban binominal. Therefore, knowledge about the temporal dynamics of GUAs is prominent in maintaining a sustainable balance between built and natural/semi-natural areas within cities. By monitoring the dynamics of green spaces and GUA per capita, unfortunate downward trends may be corrected in due time, mitigating the aforementioned negative effects of densification.

Although the importance and multi-functionality of the GUAs have been largely acknowledged, their protection and development are still parts of the larger to-do list of local and national decision-makers in Romania [28,29,30]. Proper monitoring of GUA represents a problematic issue in former communist countries [14,31]. In Romania, the National Institute of Statistics (NIS) provides official data on the size of GUAs, without offering details about the classification, the data collection process, or data validation procedures. On the other hand, the available spatial datasets (e.g., Urban Atlas) are not necessarily suited for local scale spatial analysis. These factors hinder proper monitoring of the spatial and temporal dynamics of the GUA in Romanian cities, giving way to unsustainable green-to-grey conversion practices.

This study aims to provide an example of how to perform a temporal analysis of GUAs within an unsatisfactory data context by comparing the results obtained via spatial and statistical datasets of 35 cities in Romania considering the 2006–2018 period as reference. This integration represents a compromise solution that should offer insights into GUA’s dynamics in difficult monitoring conditions determined by the absence of both high-resolution spatial datasets and metadata-enhanced statistical datasets. Specific patterns of the dynamics are identified, and differences between the spatial data and statistical data-based findings are discussed.

2 Overview of GUAs in Romania

According to the World Health Organization, the minimum accepted GUA per capita value is 9 m2, and the ideal value reaches 50 m2 of GUA per capita [22]. The targets of GUA per capita vary among European cities, as well as the GUA/person per se, from the low values characteristic to Southern European cities to the higher ones specific to mid-latitude cities [32]. In 2007, the Romanian Government decided to increase the green space per capita to at least 20 m2 by 2010 and to 26 m2 by 2013 [33]. In 2013, the term was extended until 2015 [34].

The most recent data provided by NIS show that the GUA per capita met or exceeded the target for 10 out of 40 urban administrative centres in Romania (Figure 1). The GUA per capita ranges from 5.04 m2 (Brașov City) to 42.59 m2 (Târgu Mureș). Baia Mare City was excluded from the analysis because its large GUA per capita value (131.03 m2) was considered an anomalous variable. However, as the spatial data concerning the GUA differ from the statistical ones, it is fair to assume that the number of cities that reached the set target is much lower.

Figure 1 GUA per capita in 40 Romanian urban administrative centres in 2020 (data sources: [52,53]).
Figure 1

GUA per capita in 40 Romanian urban administrative centres in 2020 (data sources: [52,53]).

This hypothesis was tested by Badiu et al. [14], who assessed the feasibility of the 26 m2 of green space per person target for 38 Romanian cities, using multiple spatial and statistical datasets (for 2008 and 2010). While the spatial data show that the target was met by just one (Iași City) of the 38 analysed urban settlements, the official statistical data indicate that eight cities fulfil this requirement. The authors conclude that the EU target cannot be universally applied and that it should take into consideration the particularities of urban settlements (e.g. the density of the built-up area, city’s importance, period of designation as an urban settlement, geomorphology settings, accessibility to major transport infrastructure), and the quality and types of green spaces. They also highlight that the categories of UGS included or excluded from the UGI are not specified, which makes the widespread use of the UGS per capita indicator reasonably problematic.

An initial reminder here is that Romania has a communist historical background and that most of its urban settlements developed due to the industrialization process that was the focus of the socialist regime, starting with the second half of the last century. Thus, there is a pattern of neglect concerning the development of the GI of Romanian cities, the priorities of the communist authorities being the industrial areas and the fast-rising residential buildings needed to accommodate as many industrial workers as possible. After 1989, when the political regime changed, the pattern persisted [35], as the economic decline experienced by most Romanian urban settlements also reduced the investments in UGI [14]. Furthermore, Gavrilidis et al. [31] reported that the UGI concept is not properly understood by the Romanian urban actors, being “mostly employed as a new synonym for green spaces,” which leads to the overlooking of the indirect incomes that UGI can provide.

Particularly problematic are the former rural settlements that have earned their current urban status during the communist period, in the endeavour to artificially increase urban population. Here, green urban spaces are dominated by domestic gardens but the parks are scarce and small [14]. Also, the cities located close to European roads present low GUA values, as their proximity to high-rank transport infrastructure favours economic growth, which is associated with an increase of the urban grey areas [14,30]. Therefore, the modern time factors that negatively affect the development of GUAs relate to the large building density [14], which in turn calls for extensive parking lots or the channelling of local funding towards infrastructure development [36].

The research focusing on the GUAs in Romanian urban settlements comprises many examples of case studies: Bucharest is most frequently selected as a study area [37,38,39,40,41,42] but major administrative urban centres like Cluj-Napoca [42], Timișoara [43,43,44,45], Craiova [46,47], Ploiești [48], Iași [28,49], and Suceava cities [28] were also analysed in terms of GUAs’ accessibility, historical evolution, or tourism potential.

In contrast, there are only a few studies that present overviews of GUA’s dynamics [35,50] or GUA-related patterns at the national scale [14,51]. Former mismatches between GUA’s sizes provided by NIS and the ones computed using GIS were identified by Badiu et al. [14] but further research on this subject should be conducted by integrating recent statistical and spatial datasets and other urban centres. Therefore, this study may be considered a continuation of the research work of Badiu et al. [14], although the scope of the two research endeavours differs, and several other analysis-related parameters are also divergent (e.g. temporal scale, selected cities, green urban space classification, and methodology). Also, research related to the urban regeneration of Romanian cities is scarce [54], there are few studies on the differences in GUA's sizes generated by the integration of different datasets in the international literature (Feltynowski et al. [55]). Since UGI-related research is only emerging in Romania Gavrilidis et al. [31] went ahead to conclude that the UGI concept failed to be properly implemented in this country; where it is considered just a reinterpretation of green spaces.

3 Methods

The study of UGI temporal dynamics may be challenging, particularly in developing countries where the concept is differently perceived by scientists [13], public authorities, and citizens [8,31,56], and the accuracy of statistical data is still a matter of debate. These factors contribute to the difficulty of answering a simple question: How green are the Romanian cities? In order to explore this matter, to detect and avoid potential errors derived from biased or inaccurate datasets, both spatial and statistical data were used to document the dynamics of GUAs (Figure 2) relating to two periods and two sets of cities: 35 cities were selected for 2012–2018, and 14 cities for 2006–2018 (Figure 3).

Figure 2 Methodological workflow.
Figure 2

Methodological workflow.

Figure 3 The Romanian cities selected for the multi-temporal analyses of GUAs.
Figure 3

The Romanian cities selected for the multi-temporal analyses of GUAs.

The reference years and study areas correspond to the status layers of the spatial dataset that were extracted from the Urban Atlas developed by Copernicus. Statistical data regarding the area of green urban spaces were extracted for the same years from the TEMPO portal developed by the Romanian NIS, in order to compare them with the spatial ones (Table 1). There are only a few examples of studies that use both spatial and statistical datasets [14], as most studies regarding the UGI in Romania prefer to use only one type of data [35,41,51]. It should be noted that the reference periods selected for the multi-temporal analysis of the GUAs dynamics specific to Romanian cities were not previously used by any other studies and they reflect the most recent available data-documented situation.

Table 1

Characteristics of the GUA spatial and statistical datasets

Dataset Reference years Minimum mapping unit Accuracy Source
Thematic accuracy Positional pixel accuracy
Spatial dataset 2006, 2012, 2018 0.0025 km2 ≥85% <±5 m Urban Atlas (2022)
Minimum mapping width: 10 m
Statistical dataset Unknown Unknown Unknown NIS (2020a)

The selected cities are rank 0 (Bucharest), rank I (11 cities), and rank II (23 cities) urban settlements, whose areas of influence extend beyond their county’s boundary. Most of them have a complex functional profile, but the smaller ones tend to focus their economic activity on certain domains (mostly services and different industrial sectors). Except for Roman and Bârlad Cities, all the other urban settlements are county administrative centres.

3.1 Spatial dataset

The spatial dataset is represented by the GUA (code 14100) extracted from Copernicus’ Urban Atlas. This category integrates “public green areas for predominantly recreational use such as gardens, zoos, parks, castle parks, and cemeteries,” as well as suburban natural areas that are managed as urban parks, fringing forests that are surrounded by urban areas and structures from at least two sides, and that is used for recreational purposes [57]. The status layer provided for 2006 does not include the cemeteries into the GUA class, but these were integrated into the 2012 and 2018 versions of the Urban Atlas. The aforementioned list of GUA features appropriately excludes other urban spaces covered by vegetation that should not be considered part of the public GI: private gardens, buildings within parks, and the patches of natural vegetation or agricultural areas surrounded by build-up areas, which are not managed as GUAs [57].

3.2 Statistical dataset

The definition of GUAs provided by the statistical dataset extracted from the TEMPO portal for the same cities is similar to the one of the Urban Atlas but some differences need to be considered. According to NIS, GUAs consist of green areas managed as parks, public gardens or squares, patches of trees and flowers, forests, cemeteries, and the courts and fields of sports facilities. The category excludes vegetable gardens, agricultural land, greenhouses, and lake surfaces [52]. The data were directly extracted from the TEMPO portal, and the processing stage comprised basic conversion operations.

The collection of these GUA-related data is not properly explained in the public documents provided by NIS; the only available information being that the process was supported by both paper and electronic questionnaires. Moreover, the NIS does not provide information about the accuracy of the statistical data or if they were verified in this regard or not (Table 1). Thus, it is impossible to compare the two datasets relating to this matter.

One key dataset difference is the inclusion of sports facilities in the statistical data but not in the spatial data. The Sports and Leisure Facilities category in the Urban Atlas encompasses various features, including those that may not be considered typical green areas, such as amusement parks, swimming resorts, and more [57]. We argue that these non-green features should not be considered part of GUAs, as they do not align with the concept of green resources or sustainable infrastructure [13]. Therefore, the spatial dataset focuses exclusively on GUA features and excludes sports fields found in the Sports and Leisure Facilities category. This difference may contribute to disparities between the two datasets and should be considered during interpretation.

3.3 Multi-temporal analysis

This study includes two multi-temporal analyses: the first analysis relates to 2012–2018 and to the 35 cities that are, mostly, the administrative centres and the most important urban settlement of their county (Figure 3). Because there are only two reference years, only three trends may be identified: (i) an upward trend of GUAs, (ii) stagnation of GUAs, and (iii) a downward trend of GUAs. The first and the last dynamics trends are analysed in order to quantify the extent of GUAs’ increase or decrease for specific cities, as shown by both datasets. The results obtained by processing the spatial and statistical datasets are compared and discussed, focusing on the discrepancies and their potential causes (Figure 2).

The second analysis integrates 2006, 2012, and 2018 as reference years, leading to nine possible dynamic patterns of GUAs (Figure 4). The GUAs in the 14 analysed cities fit one of these patterns but the findings may vary when using a certain dataset. Both the dynamic patterns and the aforementioned differences are discussed, aiming to understand the factors that determine different dynamics.

Figure 4 Possible dynamic patterns of GUAs in 2006–2018.
Figure 4

Possible dynamic patterns of GUAs in 2006–2018.

4 Results

This section separately presents the two sets of results, proceeding to their comparison. Appendix 1 includes the full spatial and statistical datasets.

4.1 Temporal dynamics of GUA according to the spatial dataset

4.1.1 2012–2018 analysis

In 2012, the GUA sizes in the 35 cities ranged from 0.199 km2 (Bistrița) to 13.55 km2 (Bucharest). By 2018, both the minimum and maximum GUA values decreased slightly to 0.195 and 13.37 km2, respectively. The total GUA values of all 35 cities increased from 45.07 km2 in 2012 to 46.73 km2 in 2018, with the average GUA growing from 1.28 to 1.33 km2 over the same period. When analysing the dynamics of GUA in each of the urban settlements, it appears that most of them (60%) followed an upward trend and that the number of cities with the same GUA for both of the reference years (8) is higher than the one of the cities where the GUA decreased (6) (Figure 5a).

Figure 5 The dynamic trends of GUAs in the 35 analysed cities (2012–2018), according to the (a) spatial dataset and (b) statistical dataset.
Figure 5

The dynamic trends of GUAs in the 35 analysed cities (2012–2018), according to the (a) spatial dataset and (b) statistical dataset.

Suceava City may be mentioned as top of the list in terms of GUA’s growth, as it registered a 236.18% increase from 2012 (0.21 km2) to 2018 (0.72 km2) (Appendix 1). Most of the cities with an upward trend of the GUA (61.90%) registered increases of 1–10% (Table 2), while the number of the urban settlements with very low (Craiova, Iași, and Slatina), medium (Bacău), or high increases (Ploiești, Târgoviște, and Tulcea) was also low (Figure 5a). At the opposite end of the spectrum, Giurgiu City registered the largest fall of the GUA (13.88%).

Table 2

Level of increase/decrease in GUAs according to the spatial and statistical datasets (2012–2018)

Percentage Spatial dataset Statistical dataset
Increase Decrease Increase Decrease
No. of cities % No. of cities % No. of cities % No. of cities %
Very low <1 % 3 14.28 3 50 3 16.66 0 0
Low 1–10 % 13 61.9 2 33.33 1 5.55 2 28.57
Medium 10–20 % 1 4.76 1 16.66 2 11.11 3 42.85
High 20–100 % 3 14.28 0 0 6 33.33 2 28.57
Very high >100 % 1 4.76 0 0 6 33.33 0 0

4.1.2 2006–2018 analysis

The total GUAs of the 14 analysed cities slightly, but constantly, increased between 2006 and 2018 from 30.19 km2 at the beginning of the reference period to 30.43 km2 in 2012 and to 30.70 km2 in 2018. This upward trend was followed by the minimum and the maximum GUA values (Piatra Neamț City, respectively, Bucharest).

Approximately 43% of the cities showed a consistent GUA increase (e.g. Arad, Brăila, Călărași, Cluj-Napoca, Oradea, and Piatra Neamț), labelled as pattern A (refer to Table 3). Pattern H, signifying a continuous GUA decrease, was observed only in Giurgiu City. Patterns C and D were applicable to an equal number of cities (about 14% each): Bacău and Craiova initially saw GUA declines, followed by increases, while the reverse trend was noted for Alba Iulia and Sibiu (refer to Figure 6a). Also according to Urban Atlas spatial data, no cities matched patterns E, F, G, or I (Table 3).

Table 3

Dynamics (2006–2018) of GUAs in the analysed cities, according to the spatial and statistical datasets

Pattern Description Spatial dataset Statistical dataset
Cities Cities
No. % No. %
A Constant increase 6 42.85 4 28.57
B Increase 2006–2012, decrease 2012–2018 3 21.42 2 14.28
C Decrease 2006–2012, increase 2012–2018 2 14.28 1 7.142
D Increase 2006–2012, equal 2012–2018 2 14.28 5 35.71
E Decrease 2006–2012, equal 2012–2018 0 0 0 0
F Equal 2006–2012, increase 2012–2018 0 0 2 14.28
G Equal 2006–2012, decrease 2012–2018 0 0 0 0
H Constant decrease 1 7.14 0 0
I Stagnation 0 0 4 28.57
Figure 6 Dynamic patterns of the 14 analysed cities (2006–2018), according to the (a) spatial dataset and (b) statistical dataset.
Figure 6

Dynamic patterns of the 14 analysed cities (2006–2018), according to the (a) spatial dataset and (b) statistical dataset.

4.2 Temporal dynamics of GUA according to the statistical dataset

4.2.1 2012–2018 analysis

The NIS data show that the minimum and the maximum GUA of the 35 cities remained the same between 2012 and 2018: 0.54 km2 for Giurgiu and 45.06 km2 for Bucharest. In contrast, the total GUA of the analysed urban settlements increased from 135.44 to 164.30 km2, and the average GUA from 3.86 km2 in 2012 to 4.69 km2 in 2018.

Approximately half of cities had increasing GUAs (51.42%), surpassing those with stagnant values (10) and decreasing values (7). The average increase (130.25%, equivalent to 1.94 km2) exceeded by far the average decrease (19.61%, equivalent to 0.86 km2). Cities maintaining the same GUA values comprised around a third (28.57%) of the total: Brașov, Bucharest, Buzău, Călărași, Focșani, Giurgiu, Slatina, Târgu Jiu, Târgu Mureș, and Timișoara (refer to Figure 5b).

Cities with notably high GUA increases make up over half of their category (66.66%). Leading cities in this regard are Baia Mare (1264.84%), Bistrița (258.69%), Tulcea (190.32%), Drobeta-Turnu Severin (136.36%), and Galați (118.26%). Although the growth in green space area is lower, it remains noteworthy, with Baia Mare and Galați adding 16.19 and 5.18 km2, respectively. A few urban areas reported minimal GUA increases (e.g. <1% for Botoșani, Craiova, and Sibiu; 1–10% for Bacău; and 10–20% for Roman and Târgoviște). Concerning the declining trend, most cities recorded a medium reduction (10–20%, e.g. Cluj-Napoca, Oradea, and Satu Mare) without extreme decreases.

4.3 2006–2018 analysis

When analysing the GI of 14 Romanian cities, it appears that their total GUA increased significantly between 2006 and 2012 (by 22.4%) and only slightly in the next 6 years from 92.55 km2 in 2012 to 93.58 km2 in 2018 (Appendix 1). The statistical dataset indicates that Giurgiu is the city with the minimum GUA, which increased between 2006 (0.34 km2) and 2012 (0.54 km2) but then remained at the same level until 2018. Bucharest, which displays the largest value of the GUA, followed a similar upward trend, followed by stagnation.

Over 30% of the cities exhibit pattern D, while 28.57% consistently increased their GUAs (e.g. Alba Iulia, Bacău, Brăila, and Piatra Neamț) (Pattern A, refer to Table 3). Patterns B and F each apply to 14.28% of the urban areas. Only one city, Sibiu, showed a GUA decrease from 2006 to 2012, followed by an increase. No cities match patterns E, G, H, or I (Table 3 and Figure 6b).

4.4 Comparison between the spatial data- and statistical data-based results

The spatial and the statistical datasets put into perspective several important differences regarding the dynamic patterns of the 35 selected cities analysed for the 2012–2018 period, and of the 14 cities for which spatial data were also available for 2006. The statistical data indicate larger GUAs than the spatial dataset, for all these 3 years (Appendix 1), with only a few exceptions. For the first reference year (2006), the spatial-based GUA is higher than the statistical-based one for three urban settlements (Alba Iulia and Arad, Giurgiu). In 2012, the number was reduced to two cities (Arad and Brașov), and 6 years later, Constanța City was added to the aforementioned pair. The value differences between the two datasets vary between 0.15 km2 (Alba Iulia) and 27.88 km2 (Bucharest) in 2006, 0.02 km2 (Giurgiu City) and 31.50 km2 (Bucharest) in 2012, 0.09 to 31.68 km2 in 2018, for the last two named cities.

The fact that the GUA in Suceava City increased by 236.18% (0.5 km2) in 2012–2018, according to the Urban Atlas but decreased by 23.29% (0.75 km2), according to the NIS data may be added to the list of striking divergence between the results provided by the spatial and statistical datasets. However, when analysing the trends specific to 2012–2018, only six cities (17.14%) register opposite trends from one dataset to the other: Bistrița, Cluj-Napoca, Constanța, Oradea, Ploiești, and Suceava (Figure 5). Almost half of the considered cities (51.42%) present different trends when considering the two sources of data, which fit one of the four possible situations: (i) the spatial dataset indicates stagnation, while the NIS data point out an upward trend of the GUA; (ii) the spatial dataset indicates stagnation, while the NIS data show a fall of the GUA; (iii) the Urban Atlas data reveal an increase of the GUA, while the statistical data point out stagnation of GUAs between 2012 and 2018; or (iv) the Urban Atlas data indicate a downward trend of the GUA, while the statistical dataset shows that the features of interest stagnated.

Referring to the latest reference period (2012–2018), it should be highlighted that spatial and statistical datasets indicate the same trends for about a third (31.42%) of the analysed urban settlements: Arad, Bacău, Brăila, Craiova, Galați, Iași, Piatra Neamț, Roman, Satu Mare, Târgoviște, and Tulcea (Figure 5). Although the values of GUAs in the considered cities varied from one data source to the other, the maintenance of the same evolution course may be counted as an element of intersection between the two sets of results.

When considering the dynamic patterns identified for the 2006–2018 period, it appears that only 2 of the 14 analysed cities fit the same pattern according to both data sources (Brăila and Piatra Neamț). In contrast, the spatial- and statistical-based data do not place any of the urban settlements into opposite pattern categories (A–H, B–C, D–E, and F–G) (Figure 6). Instead, the spatial datasets show that most of the analysed cities followed a constant upward trend, while the statistical one indicates that more cities registered an increase of their GUAs in 2006–2012, and a subsequent stagnation until 2018 (Figure 6). The most common pattern-related difference is the inclusion of three cities into the B pattern according to the spatial data, and into the D pattern according to the NIS data.

5 Discussion

5.1 Critical reflections and explanations

This study explores the differences between the temporal dynamics of the GUAs in 35 Romanian cities according to spatial and statistical datasets, also identifying possible causes for these divergences. We consider that the proposed research design is a compromise solution for the problem of data scarcity in the context of UGI in Romania and that the findings of this research work call for better, more transparent and validation-subject quantification procedures and mapping of the GUAs.

The combined GUA of the 35 analysed cities increased from 135.44 to 164.3 km2 according to NIS and from 45.07 to 46.73 km2 according to spatial data (Urban Atlas, 2022), for the same reference years (2012–2018). As observed both the starting and ending points of the GUA dynamics are higher according to the statistical dataset, as well as their increase from 2012 to 2018: 21.3%, which means 28.86 km2 according to the NIS data, and only 3.69%, which means 1.66 km2 according to the spatial dataset.

The presented divergences between the spatial data- and statistical-based results stem from a number of factors, part of which remain unknown due to the limited information concerning the process of GUA data collection performed by the NIS. As long as the minimum mapping unit used by this institution, the details about the validation process, and the associated accuracy level of the statistical data remain unknown and the goal of identifying all sources of discordance between the presented findings remains beyond reach. Therefore, the explanations for the inconsistency of the findings regarding the dynamics of the GUA must focus on the known elements, which are, generally, of technical nature. The fact that the statistical dataset provides higher values of the GUA for 78.57% of the cities in 2006, 94.28% in 2012, and 91.42% in 2018 may be explained by the convergence of several factors: (i) the 1:10 000 scale of the spatial data, (ii) the disputable data collection procedures specific to the statistical dataset, and (iii) the different components included in the spatial, respectively, the statistical datasets (e.g. the spatial dataset relies only on typical green areas like urban parks and open green spaces that are managed as urban parks, and exclude sports fields, which are included in the GUA definition and data provided by NIS).

Spatial data collection is a challenging task, whose results may be subject to misclassification of features or other technical issues [58,59]. This calls for the harmonization of land use features [59,60], for special attention dedicated to the selection of spatial datasets that fit specific research purposes [59,61] and appropriate scaling [59]. It is worth mentioning that although the Urban Atlas [62] is not an ideal spatial data source (due to its scale), it still suits the purpose of this research work better than the CORINE dataset, as the former is better at depicting small-size, sparse features, also providing a salient classification of urban features [42,61]. This is supported by Aksoy et al. [63], who argue that the Urban Atlas data can be used to further the understanding of a city’s land use/land cover changes based on local policies. Moreover, the authors explicitly refer to the green land cover of urban settlements, stating that these spatial datasets offer “an idea to local governments about how green the city is and the appropriateness of the green space-artificial space balance” [63]. Thus, despite their issues of identifying smaller parks and other GUAs, which should be, but are not included in the dataset, these datasets are the only available ones regarding the spatial distribution of the GUAs at the local scale. Although a finer mapping scale of the spatial dataset would provide more accurate results, the Urban Atlas dataset was considered suitable for conducting the multi-temporal analyses of GUA, given the fact that this study aims for a compromise solution that allows for the exploration of GUA dynamics in the scarce-data conditions specific to the Romanian cities.

In addition, data collection procedures specific to the statistical dataset may lead to biased results. Parts of the statistical yearbook data were taken from urban master plans, which are or used to be subject to various misclassifications of features and were drafted based on obsolete methodologies that do not account for urban ecology updates [64]. For example, urban parks were classified as divisions of green spaces, without subtracting the areas of irrelevant features (e.g. water bodies); in an effort to close the gap between ground reality and imposed standards. Other examples refer to the inclusion of planned roadside tree strips in the green space category, without verifying if trees were actually planted and to the inclusion of private green spaces in the green spaces category.

The exclusion of sports fields from the spatial dataset – which was preferred on the grounds of trying to accurately answer the question “How green are Romanian cities?”, may also cause a lower-value bias of this dataset. This is of particular importance when considering how the GUA of urban settlements increase in Romania: not by transforming brownfields into green areas per se, but by constructing new sports facilities, be them sports fields or indoor sports facilities. Additionally, the dataset provided by the NIS integrates tree and/or flower patches, which may be smaller than the minimum mapping unit of the Urban Atlas, but should not be classified as green spaces due to their small size and their insignificant provision of ecosystem services. This may also contribute to the gaps between the GUAs computed using the statistical and spatial datasets. The above said discrepancies may be considered part of the misunderstanding and misperception of the UGI concept among policy-makers, being linked to the obstacles of adequate UGI planning and management signalled by Gavrilidis et al. [31].

5.2 Links to previous scientific findings

The presented differences are confirmed by Badiu et al. [14], who also aimed to identify GUA patterns in Romanian cities considering different spatial and statistical data sources. Their approach differs from the present one in terms of temporal scale, selection of urban centres, and research aim. While Badiu et al. [14] performed their analysis on datasets relating to single reference years (2008 for the aerial images, TEMPO database, Environmental Protection Agencies database, and 2010 for Urban Atlas), this study sets a multi-temporal approach by analysing the dynamics of GUAs relating to two time intervals (2012–2018, 2006–2018) and two sets of urban settlements. Besides, out of the 38, respectively, 35 cities integrated in each study, only 8 of them are included in both articles. In addition, the classification of the urban green categories differs from one study to the other: Badiu et al. [14] integrated sports leisure facilities in their urban green surface layer, while the GUA computed using the spatial dataset in this study excluded them from the analysis.

The most important difference with respect to these two research works is related to their scope: the present study aims to provide an example of multi-temporal analysis in an incomplete/unsuitable data context, while Badiu et al. [14] aimed to “establish whether the indicator of UGS per capita is sufficient in the process of UGS assessment and in the context of sustainability goals, or if it is necessary to consider alternative analyses, such as patterns or determinants of UGS.” Thus, the two studies have both coalescing and diverging goals; the comparison of their findings is very useful in the endeavour to document the differences between the integrated data sources. Although Badiu et al. [14] reported some overlapping of the statistical and spatial-based results determined by the extraction of the spatial data from aerial images with a 5 m resolution, there are also cases of underestimation or overestimation of the GUA according to the NIS data.

Badiu et al. [14] argued that setting the target at 26 m2 of GUA per capita may act as a booster for the development of green spaces, but that it also creates additional pressure to fulfil this requirement, translating into an overestimation of the GUA in administrative sources. Moreover, the authors mention some unorthodox practices that artificially increase the area values of green space, among which the extension of the administrative territory boundaries to include nearby forests and the transformation of certain urban open spaces into GUAs only on paper are the most common. In this regard, Iojă et al. [56] mentioned that the land restitution that started with the new democratic regime supported the conversion of green spaces into residential areas where investments were profitable. This binominal of grey areas expansion at the expense of GUAs with central urban positions and the increase of fringing GUAs performed through territorial reorganization leads to a vicious cycle of unsustainable UGI management.

Petrișor [35] used spatial data provided by CORINE Land Cover (1990–2012) and Urban Atlas (2006–2012) to document the dynamics of GUAs in 14 Romanian cities, which were also selected for the present 2006–2018 analysis. The findings of this study were inconclusive but it is worth mentioning that, as in the present paper the Minimum Mapping Unit of the only available spatial data is not sufficient to accurately detect the smaller GUAs. This shows that green spaces within cities are very fragmented, which can affect the provision of ecosystem services [35].

Newer scientific findings also complement the ones presented in the last section. Petrișor et al. [65] studied the dynamics of open green areas (OGA) in the same 14 cities selected for the 2006–2018 analysis, using mathematical modelling and geostatistical analyses. Their findings point out higher fragmentation levels for 2012–2018 than for the 2006–2012 interval. However, this study relies on the spatial dataset of Urban Atlas, while the present one compares the data-dependent findings that result from the processing of spatial and statistical datasets. Using the dataset provided by the Urban Atlas, Petrișor et al. [66] analysed the dynamics of various components of the GI (e.g. GUAs, sports and leisure areas, agriculture areas, and natural and semi-natural areas) in the same 14 cities, aiming to “take an in-depth look at the mechanisms of change in UGI and provide concrete planning recommendations for dealing with the GI.” They conclude that the percentage of the total GI area decreased in 2006–2018 in all the analysed cities, with a higher share of loss in 2012–2018. This dynamics is related to population changes, the development of industrial parks, the approach of local administrations, and post-communist property restitution [66], all underlying the strong link between socio-economic changes and the management, monitoring, and evolution of GI.

It is of significance to emphasize that variations in the dimensions of urban green areas, stemming from the utilization of different datasets, have been documented in 18 Polish cities as reported by Feltynowski et al. [55]. In this case, the statistical datasets provided smaller sizes of green space compared to the most comprehensive dataset from the national land surveying agency. This study also highlights the need for a re-evaluation of the definitions and a broader classification of green spaces, alongside the enhancement of data quality.

5.3 Scientific contribution and limitations

This study aims to contribute to the field of UGI by analysing GUAs dynamics specific to a post-communist EU country, which still faces significant administrative issues. The integration of spatial and statistical datasets in the multi-temporal analysis of GUA should reduce the probability of obtaining data-dependent results, also pointing out how each of the datasets fails to provide a comprehensive overview of the GUA dynamics. It should be noted that this study was not aiming at an unequivocal multi-temporal overview, as this goal is unattainable due to the mapping scale of the spatial dataset and the lack of metadata specific to the statistical dataset. Also, the integration of two different sets of study areas and two reference periods may serve as an example of how to work with datasets that do not match in terms of temporal continuity.

The limitations of this study concern: (i) the coarse minimum mapping unit of the spatial dataset, which may benefit from resolution improvements that would lead to more accurate results and a superior quantification of small-size GUAs, and (ii) the untransparent statistical dataset, which should be complemented by details regarding data collection and validation procedures. These are prominent issues that need to be considered when interpreting the results. While it is important to be cautious with the numerical values, the examined patterns and significant discrepancies between the results provided by the two data sources remain valid and deserve attention. Therefore, we argue that a certain degree of skepticism is in order when looking at the obtained figures but the general insights that can be extracted from the findings – namely the large differences between the results obtained from statistical and spatial sources, are solid.

The analysis does not take into consideration either the drivers that shaped the dynamics, the structure, or the multifunctionality of the GUAs, which should also be addressed in the future. Another limitation is the fact that the study does not examine GUAs’ connectivity, which represents an important research topic in the UGI field.

Despite its limitations, the study is useful because it highlights potential errors and misinterpretations that may result from using only official datasets in order to document the dynamics of GUAs in Romanian urban settlements. The acknowledgement of this problem and of the unorthodox administrative practices that cause it, part of which may be considered a reminiscence of the communist regime, represents the first step towards elaborating and implementing uniform and transparent GUA assessments at the local scale.

6 Conclusions

This study brings to light consistent discrepancies between the GUAs computed via GIS techniques using spatial data and the ones officially reported in Romania. These may be summarized as follows:

  • Official statistical data indicate that the size of the GUAs in the selected cities is larger than the ones computed using spatial data for at least 78% of the cases, in all three reference years.

  • In 2012–2018, 68.67% of the 35 analysed cities registered different dynamics of their GUAs, from one dataset to the other.

  • In 2006–2018, only 2 of the 14 considered cities displayed the same dynamics patterns according to both spatial and statistical datasets.

The differences may be motivated by many factors: the scale of the spatial dataset, the dissimilar components that are included in the GUA category according to the two data sources, and the unknown data collection, processing, and validation procedures undertaken by the NIS in the pursuit to quantify GUAs.

Regardless of the causes that lead to the presented divergences, the dataset-dependent size of GUAs in Romanian cities may be considered just a symptom of the larger issue of inaccurate and/or incomplete databases regarding the components of urban settlements in this country. As there is no common methodology to quantify GUAs, this task is performed by each local administration as considered; a process in which various errors or accuracy issues may hinder proper GUAs’ assessment and subsequent comparisons among cities.

Thus, it is still difficult to provide an honest answer to the questions “How green are the analysed Romanian cities?” or “What is the extent of the GUAs in these cities?”. The presented findings call for improvements in the GUA quantification and mapping regulations and programmes, which should also be more transparent and undergo a salient validation procedure. National and local authorities should implement the required changes in order to transform these improvements into practices applicable at the urban scale. However, such changes should be carefully planned, considering the initial development stages of GUAs in Romania under the communist perspective, and how the implications of this early development influence the implementation of UGI-related western models [31].

Taking into consideration that the quantification of GUAs should be one of the basic tasks of UGI development, the overview of the GUA dynamics portrayed in this study represents a real predicament. Without knowledge about the spatial distribution and the multi-temporal dynamics of green spaces, the dynamic equilibrium of urban green and grey areas is hard to monitor, while the elaboration and implementation of UGI development projects are seriously impaired. Also, the absence of such information and reliable official data give way to unorthodox land conversion practices, which lead to urban densification and tilt the green–grey balance in favour of the latter. This means that in times dominated by fast-paced, uncontrolled urban growth, the development of green areas within cities remains a long-term commitment with even more far-reaching benefits to human communities.

Acknowledgements

The authors acknowledge the Operational Program Competitiveness 2014–2020, Axis 1, under POC/448/1/1 Research infrastructure projects for public R&D institutions/Sections F 2018, through the Research Center with Integrated Techniques for Atmospheric Aerosol Investigation in Romania (RECENT AIR) project, under grant agreement MySMIS no. 127324.

  1. Funding information: This research work was funded by the Faculty of Geography and Geology, Alexandru Ioan Cuza University of Iasi.

  2. Author contributions: A.C.-A.: conceptualization, data analysis, GIS, writing the first draft, revision, and editing; D.D.: conceptualization, data analysis, validation, revision, and editing.

  3. Conflict of interest: Authors state no conflict of interest.

Appendix 1 Spatial and statistical datasets of GUA in the analysed Romanian cities

A. The GUAs in the 35 cities included in the 2012–2018 analysis, according to the spatial and statistical datasets

City Spatial dataset Statistical dataset
GUA 2012 (km2) GUA 2018 (km2) Trend Difference 2012–2018 GUA 2012 (km2) GUA 2018 (km2) Trend Difference 2012–2018
km2 % km2 %
Alba Iulia 0.6214 0.6214 = 0 0 0.8000 1.1000 + 0.30 37.50
Arad 2.2254 2.2556 + 0.03 1.35 0.8900 1.0900 + 0.20 22.47
Bacău 1.0235 1.1347 + 0.11 10.86 3.7600 3.8500 + 0.09 2.39
Baia Mare 0.4640 0.4640 = 0 0 1.2800 17.4700 + 16.19 1264.84
Bârlad 0.4099 0.4099 = 0 0 1.7400 1.7100 − 0.03 1.72
Bistrița 0.1998 0.1953 − 0.005 2.26 0.9200 3.3000 + 2.38 258.69
Botoșani 0.4830 0.4830 = 0 0 2.2800 2.3000 + 0.02 0.87
Brăila 1.2761 1.3166 + 0.04 3.17 2.8300 4.7100 + 1.88 66.43
Brașov 1.5979 1.6265 + 0.02 1.78 1.4600 1.4600 = 0 0
Bucharest 13.5584 13.3776 − 0.18 1.33 45.0600 45.0600 = 0 0
Buzău 0.5457 0.5829 + 0.03 6.80 2.1000 2.1000 = 0 0
Călărași 0.5082 0.5200 + 0.01 2.32 1.8600 1.8600 = 0 0
Cluj-Napoca 1.8086 1.9839 + 0.17 9.69 9.2000 8.1400 − 1.06 11.52
Constanța 1.7089 1.7869 + 0.07 4.56 4.3000 1.5000 − 2.80 65.11
Craiova 2.3520 2.3613 + 0.00 0.39 10.3700 10.3800 + 0.01 0.09
Drobeta-Turnu Severin 0.5701 0.5701 = 0 0 1.5400 3.6400 + 2.10 136.36
Focșani 0.2594 0.2827 + 0.02 8.99 0.7000 0.7000 = 0 0
Galați 1.3112 1.3290 + 0.01 1.36 4.3800 9.5600 + 5.18 118.26
Giurgiu 0.5120 0.4409 − 0.07 13.88 0.5400 0.5400 = 0 0
Iași 1.5674 1.5792 + 0.01 0.75 4.5000 6.9600 + 2.46 54.66
Oradea 1.6674 1.8123 + 0.14 8.69 5.9700 5.1600 − 0.81 13.56
Piatra Neamț 0.2611 0.2702 + 0.009 3.47 1.9000 2.3000 + 0.40 21.05
Pitești 1.0630 1.0630 = 0 0 2.6100 3.6100 + 1.00 38.31
Ploiești 0.9731 1.4036 + 0.43 44.24 3.0200 2.8300 − 0.19 6.29
Râmnicu Vâlcea 0.2867 0.2867 = 0 0 1.1900 2.4000 + 1.21 101.68
Roman 0.4585 0.4985 + 0.04 8.72 1.0600 1.2600 + 0.20 18.86
Satu Mare 0.8830 0.8797 − 0.003 0.38 2.7900 2.3500 − 0.44 15.77
Sibiu 1.2967 1.2967 = 0 0 2.0700 2.0900 + 0.02 0.96
Slatina 0.4241 0.4279 + 0.004 0.88 1.4600 1.4600 = 0 0
Suceava 0.2158 0.7256 + 0.51 236.18 3.2200 2.4700 − 0.75 23.29
Târgoviște 0.3359 0.4346 + 0.09 29.37 1.1200 1.2400 + 0.12 10.71
Târgu Jiu 0.4228 0.4324 + 0.01 2.27 0.6000 0.6000 = 0 0
Târgu Mureș 0.7182 0.7165 − 0.002 0.23 2.0500 2.0500 = 0 0
Timișoara 2.6062 2.6016 − 0.005 0.17 5.2500 5.2500 = 0 0
Tulcea 0.4560 0.5646 + 0.10 23.82 0.6200 1.8000 + 1.18 190.32
Total 45.07 46.73 135.44 164.30
Average 1.28 1.33 3.87 4.69
Minimum 0.20 0.19 0 0 0.54 0.54 0 0
Maximum 13.55 13.37 0.51 236.18 45.06 45.06 16.19 1264.84

(+) increase, (−) decrease, and (=) stagnation.

B. The GUAs in the 14 cities included in the 2006–2018 analysis, according to the spatial and statistical datasets

City Spatial dataset Statistical dataset
GUA 2006 (km2) GUA 2012 (km2) GUA 2018 (km2) Dynamic pattern* GUA 2006 (km2) GUA 2012 (km2) GUA 2018 (km2) Dynamic pattern*
Alba Iulia 0.6186 0.6214 0.6214 D 0.4600 0.8000 1.1000 A
Arad 2.1637 2.2254 2.2556 A 0.8900 0.8900 1.0900 F
Bacău 1.0240 1.0235 1.1347 C 1.5200 3.7600 3.8500 A
Brăila 1.2568 1.2761 1.3166 A 2.7600 2.8300 4.7100 A
Bucharest 13.5025 13.5584 13.3776 B 41.3900 45.0600 45.0600 D
Călărași 0.4614 0.5082 0.5200 A 1.4700 1.8600 1.8600 D
Cluj-Napoca 1.7917 1.8086 1.9839 A 3.6200 9.2000 8.1400 B
Craiova 2.4031 2.3520 2.3613 C 10.3700 10.3700 10.3800 F
Giurgiu 0.5487 0.5120 0.4409 H 0.3400 0.5400 0.5400 D
Oradea 1.6209 1.6674 1.8123 A 2.2000 5.9700 5.1600 B
Piatra Neamț 0.2428 0.2611 0.2702 A 1.7200 1.9000 2.3000 A
Sibiu 1.2966 1.2967 1.2967 D 2.2500 2.0700 2.0900 C
Târgu Mureș 0.7062 0.7182 0.7165 B 1.6000 2.0500 2.0500 D
Timișoara 2.5615 2.6062 2.6016 B 5.0200 5.2500 5.2500 D
Total 30.19 30.43 30.71 75.61 92.55 93.58
Average 2.15 2.17 2.19 5.40 6.61 6.68
Minimum 0.24 0.26 0.27 0.34 0.54 0.54
Maximum 13.50 13.55 13.37 41.39 45.06 45.06

*Letters stand for the possible dynamic patterns of GUAs in 2006–2018, as shown in Figures 4 and 6.

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Received: 2023-08-18
Revised: 2023-10-25
Accepted: 2023-10-27
Published Online: 2023-12-19

© 2023 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

Articles in the same Issue

  1. Regular Articles
  2. Diagenesis and evolution of deep tight reservoirs: A case study of the fourth member of Shahejie Formation (cg: 50.4-42 Ma) in Bozhong Sag
  3. Petrography and mineralogy of the Oligocene flysch in Ionian Zone, Albania: Implications for the evolution of sediment provenance and paleoenvironment
  4. Biostratigraphy of the Late Campanian–Maastrichtian of the Duwi Basin, Red Sea, Egypt
  5. Structural deformation and its implication for hydrocarbon accumulation in the Wuxia fault belt, northwestern Junggar basin, China
  6. Carbonate texture identification using multi-layer perceptron neural network
  7. Metallogenic model of the Hongqiling Cu–Ni sulfide intrusions, Central Asian Orogenic Belt: Insight from long-period magnetotellurics
  8. Assessments of recent Global Geopotential Models based on GPS/levelling and gravity data along coastal zones of Egypt
  9. Accuracy assessment and improvement of SRTM, ASTER, FABDEM, and MERIT DEMs by polynomial and optimization algorithm: A case study (Khuzestan Province, Iran)
  10. Uncertainty assessment of 3D geological models based on spatial diffusion and merging model
  11. Evaluation of dynamic behavior of varved clays from the Warsaw ice-dammed lake, Poland
  12. Impact of AMSU-A and MHS radiances assimilation on Typhoon Megi (2016) forecasting
  13. Contribution to the building of a weather information service for solar panel cleaning operations at Diass plant (Senegal, Western Sahel)
  14. Measuring spatiotemporal accessibility to healthcare with multimodal transport modes in the dynamic traffic environment
  15. Mathematical model for conversion of groundwater flow from confined to unconfined aquifers with power law processes
  16. NSP variation on SWAT with high-resolution data: A case study
  17. Reconstruction of paleoglacial equilibrium-line altitudes during the Last Glacial Maximum in the Diancang Massif, Northwest Yunnan Province, China
  18. A prediction model for Xiangyang Neolithic sites based on a random forest algorithm
  19. Determining the long-term impact area of coastal thermal discharge based on a harmonic model of sea surface temperature
  20. Origin of block accumulations based on the near-surface geophysics
  21. Investigating the limestone quarries as geoheritage sites: Case of Mardin ancient quarry
  22. Population genetics and pedigree geography of Trionychia japonica in the four mountains of Henan Province and the Taihang Mountains
  23. Performance audit evaluation of marine development projects based on SPA and BP neural network model
  24. Study on the Early Cretaceous fluvial-desert sedimentary paleogeography in the Northwest of Ordos Basin
  25. Detecting window line using an improved stacked hourglass network based on new real-world building façade dataset
  26. Automated identification and mapping of geological folds in cross sections
  27. Silicate and carbonate mixed shelf formation and its controlling factors, a case study from the Cambrian Canglangpu formation in Sichuan basin, China
  28. Ground penetrating radar and magnetic gradient distribution approach for subsurface investigation of solution pipes in post-glacial settings
  29. Research on pore structures of fine-grained carbonate reservoirs and their influence on waterflood development
  30. Risk assessment of rain-induced debris flow in the lower reaches of Yajiang River based on GIS and CF coupling models
  31. Multifractal analysis of temporal and spatial characteristics of earthquakes in Eurasian seismic belt
  32. Surface deformation and damage of 2022 (M 6.8) Luding earthquake in China and its tectonic implications
  33. Differential analysis of landscape patterns of land cover products in tropical marine climate zones – A case study in Malaysia
  34. DEM-based analysis of tectonic geomorphologic characteristics and tectonic activity intensity of the Dabanghe River Basin in South China Karst
  35. Distribution, pollution levels, and health risk assessment of heavy metals in groundwater in the main pepper production area of China
  36. Study on soil quality effect of reconstructing by Pisha sandstone and sand soil
  37. Understanding the characteristics of loess strata and quaternary climate changes in Luochuan, Shaanxi Province, China, through core analysis
  38. Dynamic variation of groundwater level and its influencing factors in typical oasis irrigated areas in Northwest China
  39. Creating digital maps for geotechnical characteristics of soil based on GIS technology and remote sensing
  40. Changes in the course of constant loading consolidation in soil with modeled granulometric composition contaminated with petroleum substances
  41. Correlation between the deformation of mineral crystal structures and fault activity: A case study of the Yingxiu-Beichuan fault and the Milin fault
  42. Cognitive characteristics of the Qiang religious culture and its influencing factors in Southwest China
  43. Spatiotemporal variation characteristics analysis of infrastructure iron stock in China based on nighttime light data
  44. Interpretation of aeromagnetic and remote sensing data of Auchi and Idah sheets of the Benin-arm Anambra basin: Implication of mineral resources
  45. Building element recognition with MTL-AINet considering view perspectives
  46. Characteristics of the present crustal deformation in the Tibetan Plateau and its relationship with strong earthquakes
  47. Influence of fractures in tight sandstone oil reservoir on hydrocarbon accumulation: A case study of Yanchang Formation in southeastern Ordos Basin
  48. Nutrient assessment and land reclamation in the Loess hills and Gulch region in the context of gully control
  49. Handling imbalanced data in supervised machine learning for lithological mapping using remote sensing and airborne geophysical data
  50. Spatial variation of soil nutrients and evaluation of cultivated land quality based on field scale
  51. Lignin analysis of sediments from around 2,000 to 1,000 years ago (Jiulong River estuary, southeast China)
  52. Assessing OpenStreetMap roads fitness-for-use for disaster risk assessment in developing countries: The case of Burundi
  53. Transforming text into knowledge graph: Extracting and structuring information from spatial development plans
  54. A symmetrical exponential model of soil temperature in temperate steppe regions of China
  55. A landslide susceptibility assessment method based on auto-encoder improved deep belief network
  56. Numerical simulation analysis of ecological monitoring of small reservoir dam based on maximum entropy algorithm
  57. Morphometry of the cold-climate Bory Stobrawskie Dune Field (SW Poland): Evidence for multi-phase Lateglacial aeolian activity within the European Sand Belt
  58. Adopting a new approach for finding missing people using GIS techniques: A case study in Saudi Arabia’s desert area
  59. Geological earthquake simulations generated by kinematic heterogeneous energy-based method: Self-arrested ruptures and asperity criterion
  60. Semi-automated classification of layered rock slopes using digital elevation model and geological map
  61. Geochemical characteristics of arc fractionated I-type granitoids of eastern Tak Batholith, Thailand
  62. Lithology classification of igneous rocks using C-band and L-band dual-polarization SAR data
  63. Analysis of artificial intelligence approaches to predict the wall deflection induced by deep excavation
  64. Evaluation of the current in situ stress in the middle Permian Maokou Formation in the Longnüsi area of the central Sichuan Basin, China
  65. Utilizing microresistivity image logs to recognize conglomeratic channel architectural elements of Baikouquan Formation in slope of Mahu Sag
  66. Resistivity cutoff of low-resistivity and low-contrast pays in sandstone reservoirs from conventional well logs: A case of Paleogene Enping Formation in A-Oilfield, Pearl River Mouth Basin, South China Sea
  67. Examining the evacuation routes of the sister village program by using the ant colony optimization algorithm
  68. Spatial objects classification using machine learning and spatial walk algorithm
  69. Study on the stabilization mechanism of aeolian sandy soil formation by adding a natural soft rock
  70. Bump feature detection of the road surface based on the Bi-LSTM
  71. The origin and evolution of the ore-forming fluids at the Manondo-Choma gold prospect, Kirk range, southern Malawi
  72. A retrieval model of surface geochemistry composition based on remotely sensed data
  73. Exploring the spatial dynamics of cultural facilities based on multi-source data: A case study of Nanjing’s art institutions
  74. Study of pore-throat structure characteristics and fluid mobility of Chang 7 tight sandstone reservoir in Jiyuan area, Ordos Basin
  75. Study of fracturing fluid re-discharge based on percolation experiments and sampling tests – An example of Fuling shale gas Jiangdong block, China
  76. Impacts of marine cloud brightening scheme on climatic extremes in the Tibetan Plateau
  77. Ecological protection on the West Coast of Taiwan Strait under economic zone construction: A case study of land use in Yueqing
  78. The time-dependent deformation and damage constitutive model of rock based on dynamic disturbance tests
  79. Evaluation of spatial form of rural ecological landscape and vulnerability of water ecological environment based on analytic hierarchy process
  80. Fingerprint of magma mixture in the leucogranites: Spectroscopic and petrochemical approach, Kalebalta-Central Anatolia, Türkiye
  81. Principles of self-calibration and visual effects for digital camera distortion
  82. UAV-based doline mapping in Brazilian karst: A cave heritage protection reconnaissance
  83. Evaluation and low carbon ecological urban–rural planning and construction based on energy planning mechanism
  84. Modified non-local means: A novel denoising approach to process gravity field data
  85. A novel travel route planning method based on an ant colony optimization algorithm
  86. Effect of time-variant NDVI on landside susceptibility: A case study in Quang Ngai province, Vietnam
  87. Regional tectonic uplift indicated by geomorphological parameters in the Bahe River Basin, central China
  88. Computer information technology-based green excavation of tunnels in complex strata and technical decision of deformation control
  89. Spatial evolution of coastal environmental enterprises: An exploration of driving factors in Jiangsu Province
  90. A comparative assessment and geospatial simulation of three hydrological models in urban basins
  91. Aquaculture industry under the blue transformation in Jiangsu, China: Structure evolution and spatial agglomeration
  92. Quantitative and qualitative interpretation of community partitions by map overlaying and calculating the distribution of related geographical features
  93. Numerical investigation of gravity-grouted soil-nail pullout capacity in sand
  94. Analysis of heavy pollution weather in Shenyang City and numerical simulation of main pollutants
  95. Road cut slope stability analysis for static and dynamic (pseudo-static analysis) loading conditions
  96. Forest biomass assessment combining field inventorying and remote sensing data
  97. Late Jurassic Haobugao granites from the southern Great Xing’an Range, NE China: Implications for postcollision extension of the Mongol–Okhotsk Ocean
  98. Petrogenesis of the Sukadana Basalt based on petrology and whole rock geochemistry, Lampung, Indonesia: Geodynamic significances
  99. Numerical study on the group wall effect of nodular diaphragm wall foundation in high-rise buildings
  100. Water resources utilization and tourism environment assessment based on water footprint
  101. Geochemical evaluation of the carbonaceous shale associated with the Permian Mikambeni Formation of the Tuli Basin for potential gas generation, South Africa
  102. Detection and characterization of lineaments using gravity data in the south-west Cameroon zone: Hydrogeological implications
  103. Study on spatial pattern of tourism landscape resources in county cities of Yangtze River Economic Belt
  104. The effect of weathering on drillability of dolomites
  105. Noise masking of near-surface scattering (heterogeneities) on subsurface seismic reflectivity
  106. Query optimization-oriented lateral expansion method of distributed geological borehole database
  107. Petrogenesis of the Morobe Granodiorite and their shoshonitic mafic microgranular enclaves in Maramuni arc, Papua New Guinea
  108. Environmental health risk assessment of urban water sources based on fuzzy set theory
  109. Spatial distribution of urban basic education resources in Shanghai: Accessibility and supply-demand matching evaluation
  110. Spatiotemporal changes in land use and residential satisfaction in the Huai River-Gaoyou Lake Rim area
  111. Walkaway vertical seismic profiling first-arrival traveltime tomography with velocity structure constraints
  112. Study on the evaluation system and risk factor traceability of receiving water body
  113. Predicting copper-polymetallic deposits in Kalatag using the weight of evidence model and novel data sources
  114. Temporal dynamics of green urban areas in Romania. A comparison between spatial and statistical data
  115. Passenger flow forecast of tourist attraction based on MACBL in LBS big data environment
  116. Varying particle size selectivity of soil erosion along a cultivated catena
  117. Relationship between annual soil erosion and surface runoff in Wadi Hanifa sub-basins
  118. Influence of nappe structure on the Carboniferous volcanic reservoir in the middle of the Hongche Fault Zone, Junggar Basin, China
  119. Dynamic analysis of MSE wall subjected to surface vibration loading
  120. Pre-collisional architecture of the European distal margin: Inferences from the high-pressure continental units of central Corsica (France)
  121. The interrelation of natural diversity with tourism in Kosovo
  122. Assessment of geosites as a basis for geotourism development: A case study of the Toplica District, Serbia
  123. IG-YOLOv5-based underwater biological recognition and detection for marine protection
  124. Monitoring drought dynamics using remote sensing-based combined drought index in Ergene Basin, Türkiye
  125. Review Articles
  126. The actual state of the geodetic and cartographic resources and legislation in Poland
  127. Evaluation studies of the new mining projects
  128. Comparison and significance of grain size parameters of the Menyuan loess calculated using different methods
  129. Scientometric analysis of flood forecasting for Asia region and discussion on machine learning methods
  130. Rainfall-induced transportation embankment failure: A review
  131. Rapid Communication
  132. Branch fault discovered in Tangshan fault zone on the Kaiping-Guye boundary, North China
  133. Technical Note
  134. Introducing an intelligent multi-level retrieval method for mineral resource potential evaluation result data
  135. Erratum
  136. Erratum to “Forest cover assessment using remote-sensing techniques in Crete Island, Greece”
  137. Addendum
  138. The relationship between heat flow and seismicity in global tectonically active zones
  139. Commentary
  140. Improved entropy weight methods and their comparisons in evaluating the high-quality development of Qinghai, China
  141. Special Issue: Geoethics 2022 - Part II
  142. Loess and geotourism potential of the Braničevo District (NE Serbia): From overexploitation to paleoclimate interpretation
https://doi.org/10.1515/geo-2022-0574
Keywords for this article
urban green infrastructure; green urban spaces; Urban dynamics; spatial analysis; Romania
Creative Commons
BY 4.0

Articles in the same Issue

  1. Regular Articles
  2. Diagenesis and evolution of deep tight reservoirs: A case study of the fourth member of Shahejie Formation (cg: 50.4-42 Ma) in Bozhong Sag
  3. Petrography and mineralogy of the Oligocene flysch in Ionian Zone, Albania: Implications for the evolution of sediment provenance and paleoenvironment
  4. Biostratigraphy of the Late Campanian–Maastrichtian of the Duwi Basin, Red Sea, Egypt
  5. Structural deformation and its implication for hydrocarbon accumulation in the Wuxia fault belt, northwestern Junggar basin, China
  6. Carbonate texture identification using multi-layer perceptron neural network
  7. Metallogenic model of the Hongqiling Cu–Ni sulfide intrusions, Central Asian Orogenic Belt: Insight from long-period magnetotellurics
  8. Assessments of recent Global Geopotential Models based on GPS/levelling and gravity data along coastal zones of Egypt
  9. Accuracy assessment and improvement of SRTM, ASTER, FABDEM, and MERIT DEMs by polynomial and optimization algorithm: A case study (Khuzestan Province, Iran)
  10. Uncertainty assessment of 3D geological models based on spatial diffusion and merging model
  11. Evaluation of dynamic behavior of varved clays from the Warsaw ice-dammed lake, Poland
  12. Impact of AMSU-A and MHS radiances assimilation on Typhoon Megi (2016) forecasting
  13. Contribution to the building of a weather information service for solar panel cleaning operations at Diass plant (Senegal, Western Sahel)
  14. Measuring spatiotemporal accessibility to healthcare with multimodal transport modes in the dynamic traffic environment
  15. Mathematical model for conversion of groundwater flow from confined to unconfined aquifers with power law processes
  16. NSP variation on SWAT with high-resolution data: A case study
  17. Reconstruction of paleoglacial equilibrium-line altitudes during the Last Glacial Maximum in the Diancang Massif, Northwest Yunnan Province, China
  18. A prediction model for Xiangyang Neolithic sites based on a random forest algorithm
  19. Determining the long-term impact area of coastal thermal discharge based on a harmonic model of sea surface temperature
  20. Origin of block accumulations based on the near-surface geophysics
  21. Investigating the limestone quarries as geoheritage sites: Case of Mardin ancient quarry
  22. Population genetics and pedigree geography of Trionychia japonica in the four mountains of Henan Province and the Taihang Mountains
  23. Performance audit evaluation of marine development projects based on SPA and BP neural network model
  24. Study on the Early Cretaceous fluvial-desert sedimentary paleogeography in the Northwest of Ordos Basin
  25. Detecting window line using an improved stacked hourglass network based on new real-world building façade dataset
  26. Automated identification and mapping of geological folds in cross sections
  27. Silicate and carbonate mixed shelf formation and its controlling factors, a case study from the Cambrian Canglangpu formation in Sichuan basin, China
  28. Ground penetrating radar and magnetic gradient distribution approach for subsurface investigation of solution pipes in post-glacial settings
  29. Research on pore structures of fine-grained carbonate reservoirs and their influence on waterflood development
  30. Risk assessment of rain-induced debris flow in the lower reaches of Yajiang River based on GIS and CF coupling models
  31. Multifractal analysis of temporal and spatial characteristics of earthquakes in Eurasian seismic belt
  32. Surface deformation and damage of 2022 (M 6.8) Luding earthquake in China and its tectonic implications
  33. Differential analysis of landscape patterns of land cover products in tropical marine climate zones – A case study in Malaysia
  34. DEM-based analysis of tectonic geomorphologic characteristics and tectonic activity intensity of the Dabanghe River Basin in South China Karst
  35. Distribution, pollution levels, and health risk assessment of heavy metals in groundwater in the main pepper production area of China
  36. Study on soil quality effect of reconstructing by Pisha sandstone and sand soil
  37. Understanding the characteristics of loess strata and quaternary climate changes in Luochuan, Shaanxi Province, China, through core analysis
  38. Dynamic variation of groundwater level and its influencing factors in typical oasis irrigated areas in Northwest China
  39. Creating digital maps for geotechnical characteristics of soil based on GIS technology and remote sensing
  40. Changes in the course of constant loading consolidation in soil with modeled granulometric composition contaminated with petroleum substances
  41. Correlation between the deformation of mineral crystal structures and fault activity: A case study of the Yingxiu-Beichuan fault and the Milin fault
  42. Cognitive characteristics of the Qiang religious culture and its influencing factors in Southwest China
  43. Spatiotemporal variation characteristics analysis of infrastructure iron stock in China based on nighttime light data
  44. Interpretation of aeromagnetic and remote sensing data of Auchi and Idah sheets of the Benin-arm Anambra basin: Implication of mineral resources
  45. Building element recognition with MTL-AINet considering view perspectives
  46. Characteristics of the present crustal deformation in the Tibetan Plateau and its relationship with strong earthquakes
  47. Influence of fractures in tight sandstone oil reservoir on hydrocarbon accumulation: A case study of Yanchang Formation in southeastern Ordos Basin
  48. Nutrient assessment and land reclamation in the Loess hills and Gulch region in the context of gully control
  49. Handling imbalanced data in supervised machine learning for lithological mapping using remote sensing and airborne geophysical data
  50. Spatial variation of soil nutrients and evaluation of cultivated land quality based on field scale
  51. Lignin analysis of sediments from around 2,000 to 1,000 years ago (Jiulong River estuary, southeast China)
  52. Assessing OpenStreetMap roads fitness-for-use for disaster risk assessment in developing countries: The case of Burundi
  53. Transforming text into knowledge graph: Extracting and structuring information from spatial development plans
  54. A symmetrical exponential model of soil temperature in temperate steppe regions of China
  55. A landslide susceptibility assessment method based on auto-encoder improved deep belief network
  56. Numerical simulation analysis of ecological monitoring of small reservoir dam based on maximum entropy algorithm
  57. Morphometry of the cold-climate Bory Stobrawskie Dune Field (SW Poland): Evidence for multi-phase Lateglacial aeolian activity within the European Sand Belt
  58. Adopting a new approach for finding missing people using GIS techniques: A case study in Saudi Arabia’s desert area
  59. Geological earthquake simulations generated by kinematic heterogeneous energy-based method: Self-arrested ruptures and asperity criterion
  60. Semi-automated classification of layered rock slopes using digital elevation model and geological map
  61. Geochemical characteristics of arc fractionated I-type granitoids of eastern Tak Batholith, Thailand
  62. Lithology classification of igneous rocks using C-band and L-band dual-polarization SAR data
  63. Analysis of artificial intelligence approaches to predict the wall deflection induced by deep excavation
  64. Evaluation of the current in situ stress in the middle Permian Maokou Formation in the Longnüsi area of the central Sichuan Basin, China
  65. Utilizing microresistivity image logs to recognize conglomeratic channel architectural elements of Baikouquan Formation in slope of Mahu Sag
  66. Resistivity cutoff of low-resistivity and low-contrast pays in sandstone reservoirs from conventional well logs: A case of Paleogene Enping Formation in A-Oilfield, Pearl River Mouth Basin, South China Sea
  67. Examining the evacuation routes of the sister village program by using the ant colony optimization algorithm
  68. Spatial objects classification using machine learning and spatial walk algorithm
  69. Study on the stabilization mechanism of aeolian sandy soil formation by adding a natural soft rock
  70. Bump feature detection of the road surface based on the Bi-LSTM
  71. The origin and evolution of the ore-forming fluids at the Manondo-Choma gold prospect, Kirk range, southern Malawi
  72. A retrieval model of surface geochemistry composition based on remotely sensed data
  73. Exploring the spatial dynamics of cultural facilities based on multi-source data: A case study of Nanjing’s art institutions
  74. Study of pore-throat structure characteristics and fluid mobility of Chang 7 tight sandstone reservoir in Jiyuan area, Ordos Basin
  75. Study of fracturing fluid re-discharge based on percolation experiments and sampling tests – An example of Fuling shale gas Jiangdong block, China
  76. Impacts of marine cloud brightening scheme on climatic extremes in the Tibetan Plateau
  77. Ecological protection on the West Coast of Taiwan Strait under economic zone construction: A case study of land use in Yueqing
  78. The time-dependent deformation and damage constitutive model of rock based on dynamic disturbance tests
  79. Evaluation of spatial form of rural ecological landscape and vulnerability of water ecological environment based on analytic hierarchy process
  80. Fingerprint of magma mixture in the leucogranites: Spectroscopic and petrochemical approach, Kalebalta-Central Anatolia, Türkiye
  81. Principles of self-calibration and visual effects for digital camera distortion
  82. UAV-based doline mapping in Brazilian karst: A cave heritage protection reconnaissance
  83. Evaluation and low carbon ecological urban–rural planning and construction based on energy planning mechanism
  84. Modified non-local means: A novel denoising approach to process gravity field data
  85. A novel travel route planning method based on an ant colony optimization algorithm
  86. Effect of time-variant NDVI on landside susceptibility: A case study in Quang Ngai province, Vietnam
  87. Regional tectonic uplift indicated by geomorphological parameters in the Bahe River Basin, central China
  88. Computer information technology-based green excavation of tunnels in complex strata and technical decision of deformation control
  89. Spatial evolution of coastal environmental enterprises: An exploration of driving factors in Jiangsu Province
  90. A comparative assessment and geospatial simulation of three hydrological models in urban basins
  91. Aquaculture industry under the blue transformation in Jiangsu, China: Structure evolution and spatial agglomeration
  92. Quantitative and qualitative interpretation of community partitions by map overlaying and calculating the distribution of related geographical features
  93. Numerical investigation of gravity-grouted soil-nail pullout capacity in sand
  94. Analysis of heavy pollution weather in Shenyang City and numerical simulation of main pollutants
  95. Road cut slope stability analysis for static and dynamic (pseudo-static analysis) loading conditions
  96. Forest biomass assessment combining field inventorying and remote sensing data
  97. Late Jurassic Haobugao granites from the southern Great Xing’an Range, NE China: Implications for postcollision extension of the Mongol–Okhotsk Ocean
  98. Petrogenesis of the Sukadana Basalt based on petrology and whole rock geochemistry, Lampung, Indonesia: Geodynamic significances
  99. Numerical study on the group wall effect of nodular diaphragm wall foundation in high-rise buildings
  100. Water resources utilization and tourism environment assessment based on water footprint
  101. Geochemical evaluation of the carbonaceous shale associated with the Permian Mikambeni Formation of the Tuli Basin for potential gas generation, South Africa
  102. Detection and characterization of lineaments using gravity data in the south-west Cameroon zone: Hydrogeological implications
  103. Study on spatial pattern of tourism landscape resources in county cities of Yangtze River Economic Belt
  104. The effect of weathering on drillability of dolomites
  105. Noise masking of near-surface scattering (heterogeneities) on subsurface seismic reflectivity
  106. Query optimization-oriented lateral expansion method of distributed geological borehole database
  107. Petrogenesis of the Morobe Granodiorite and their shoshonitic mafic microgranular enclaves in Maramuni arc, Papua New Guinea
  108. Environmental health risk assessment of urban water sources based on fuzzy set theory
  109. Spatial distribution of urban basic education resources in Shanghai: Accessibility and supply-demand matching evaluation
  110. Spatiotemporal changes in land use and residential satisfaction in the Huai River-Gaoyou Lake Rim area
  111. Walkaway vertical seismic profiling first-arrival traveltime tomography with velocity structure constraints
  112. Study on the evaluation system and risk factor traceability of receiving water body
  113. Predicting copper-polymetallic deposits in Kalatag using the weight of evidence model and novel data sources
  114. Temporal dynamics of green urban areas in Romania. A comparison between spatial and statistical data
  115. Passenger flow forecast of tourist attraction based on MACBL in LBS big data environment
  116. Varying particle size selectivity of soil erosion along a cultivated catena
  117. Relationship between annual soil erosion and surface runoff in Wadi Hanifa sub-basins
  118. Influence of nappe structure on the Carboniferous volcanic reservoir in the middle of the Hongche Fault Zone, Junggar Basin, China
  119. Dynamic analysis of MSE wall subjected to surface vibration loading
  120. Pre-collisional architecture of the European distal margin: Inferences from the high-pressure continental units of central Corsica (France)
  121. The interrelation of natural diversity with tourism in Kosovo
  122. Assessment of geosites as a basis for geotourism development: A case study of the Toplica District, Serbia
  123. IG-YOLOv5-based underwater biological recognition and detection for marine protection
  124. Monitoring drought dynamics using remote sensing-based combined drought index in Ergene Basin, Türkiye
  125. Review Articles
  126. The actual state of the geodetic and cartographic resources and legislation in Poland
  127. Evaluation studies of the new mining projects
  128. Comparison and significance of grain size parameters of the Menyuan loess calculated using different methods
  129. Scientometric analysis of flood forecasting for Asia region and discussion on machine learning methods
  130. Rainfall-induced transportation embankment failure: A review
  131. Rapid Communication
  132. Branch fault discovered in Tangshan fault zone on the Kaiping-Guye boundary, North China
  133. Technical Note
  134. Introducing an intelligent multi-level retrieval method for mineral resource potential evaluation result data
  135. Erratum
  136. Erratum to “Forest cover assessment using remote-sensing techniques in Crete Island, Greece”
  137. Addendum
  138. The relationship between heat flow and seismicity in global tectonically active zones
  139. Commentary
  140. Improved entropy weight methods and their comparisons in evaluating the high-quality development of Qinghai, China
  141. Special Issue: Geoethics 2022 - Part II
  142. Loess and geotourism potential of the Braničevo District (NE Serbia): From overexploitation to paleoclimate interpretation
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