Pemetaan Cepat Kawasan Terdampak Bencana Longsor dan Banjir di Kabupaten Bangli, Provinsi Bali

Abstrak Teknologi penginderaan jauh mengalami perkembangan yang sangat pesat. Salah satunya adalah teknologi akuisisi data dengan menggunakan UAV (Unmanned Aerial Vehicle).  Teknologi UAV dapat dipergunakan dalam berbagai bidang, salah satunya adalah bidang kebencanaan. Tujuan penelitian ini adalah untuk melakukan pemetaan secara cepat kawasan terdampak bencana banjir dan longsor di Kabupaten Bangli, Bali dengan menggunakan teknologi UAV. Metode yang digunakan adalah pemotretan udara dengan UAV, survei lapangan dan analisis laboratorium. Pemotretan udara dilakukan satu hari pasca kejadian longsor dengan ketinggian jelajah pesawat antara 100-120 meter di atas permukaan tanah. Resolusi spasial yang dihasilkan antara 4,5 - 6,5 cm. Wilayah yang berhasil dipetakan adalah wilayah yang terdampak banjir dan longsor di Desa Songan A serta Songan B, wilayah terdampak banjir bandang Yeh Mampeh di Desa Batur Selatan, serta wilayah terdampak longsor di Desa Sukawana dan Desa Awan. Berdasarkan hasil pemotretan udara, dapat diketahui luasan daerah terdampak longsor. Lebih lanjut, strategi rehabilitasi dan rekonstruksi dapat dilakukan dengan menggunakan hasil pemotretan udara.  Abstrak Remote sensing technology is experiencing rapid developments. One of which is in the field of data acquisition that has currently adopted the use of Unmanned Aerial Vehicle (UAV). UAV technology is, for instance, employed in various studies related to disasters. This research aimed to perform a rapid mapping of flood- and landslide-affected areas in Bangli Regency, Bali using UAV technology. The applied methods included UAV-assisted aerial photography, field survey, and laboratory analysis. The aerial photography was conducted one day after the landslide event and at a recording altitude of 100-120 m above the ground. The spatial resolution produced in the photography was 4.5-6.5 cm. The mapped areas were the ones affected by floods and landslides in Songa A and Songa B Villages, flash floods in Yeh Mampeh, Batur Selatan Village, and landslides in Sukawana and Awan Villages. The aerial photography also provided the extent of the landslide-affected areas. Therefore, the post-disaster rehabilitation and reconstruction strategies can be implemented using the results of the aerial photography.

Measuring polycentric urban development : the importance of accurately determining the ‘balance’ between ‘centers’

In recent years, much research has been devoted to developing appropriate analytical frameworks to capture polycentric urban development (PUD). In a recent contribution to this journal, Bartosiewicz and Marcińczak (2020) present what is arguably the most comprehensive, comparative review to date of the degree to which different analytical frameworks produce consistent results. The purpose of this research note is to show why we believe parts of Bartosiewicz and Marcińczak's (2020) findings need nuance and qualification. Our starting point is that a useful comparison between different studies and measurement frameworks needs to consider the relevance of consistency in several key dimensions, two of which are particularly pertinent here: (1) the careful specification of what constitutes a ‘center’ in a polycentric urban system, and (2) the identification of the ‘balance’ between centers as a measure of the degree of polycentricity. Two brief empirical analyses of the degree of morphological polycentricity in Polish NUTS-3 areas and the Chinese city-regions along the ‘Yangtze Economic Belt’ are included. Finally, suggestions are provided to facilitate future comparative analyses of PUD

Beyond the urban-rural dichotomy:Towards a more nuanced analysis of changes in built-up land

Urban land and rural land are typically represented as homogenous and mutually exclusive classes in land change analyses. As a result, differences in urban land use intensity, as well as mosaic landscapes combining urban and rural land uses are not represented. In this study we explore the distribution of urban land and urban land use intensity in Europe and the changes therein. Specifically, we analyze the distribution of built-up land within pixels of 1 km2. At that resolution we find that most built-up land is distributed over predominantly non-built-up pixels. Consistently, we find that most urban land use changes between 2000 and 2014 come in small incremental changes, rather than sudden large-scale conversions from rural to urban land. Using urban population densities, we find that urban land use intensity varies strongly across 1 km2 pixels in Europe, as illustrated by a coefficient of variation of 85%. We found a similarly high variation between urban population densities for most individual countries and within areas with the same share of built-up land. Population changes have led to different combinations of urban land expansion and urban intensity changes in different study periods (1975–1990, 1990–2000, and 2000–2015) and countries. These findings suggest that land use change models could be improved by more nuanced representations of urban land, including mosaic classes and different urban land use intensities

Vom Siedlungsbrei zum Städtischen? Eine mehrdimensionale Bestandsaufnahme der Suburbanisierung

Angesichts der Reurbanisierung deutscher Großstädte stellt sich die Frage, ob, wo, mit welcher Intensität und welchen Tendenzen es noch Suburbanisierung gibt. Ein mehrdimensionales quantitatives Modell liefert hierzu Antworten. Zusätzlich zur Bevölkerungsentwicklung, die häufig auf Reurbanisierung oder Suburbanisierung hinweist, integriert das vorgestellte Modell die Beschäftigten- und die Flächenentwicklung. Über Stadt-Umland- Relationen hinausgehend wird untersucht, ob sich die Entwicklungen im Umland großer Städte gemäß dem Idealbild dezentraler Konzentration auf die dortigen Mittelstädte konzentrieren oder disperser auf kleinere Städte und Gemeinden verteilen. Die Berechnungen münden in einen Suburbanisierungsindex, der sich jeweils im Abstand einiger Jahre periodisch ermitteln lässt (Monitoring). Jenseits einer hauptsächlich auf die Bevölkerungsentwicklung gerichteten Perspektive wird das Bild der Suburbanisierung differenziert und vervollständigt. Der Vergleich der unterschiedlichen Dimensionen (Bevölkerung, Beschäftigte, Fläche) führt zu teils gegenläufigen Ergebnissen.In view of a phase of reurbanisation of German large cities, in this paper the question is raised, whether, where, with which intensity and tendency suburbanisation still can be found. Answers will be delivered by a multidimensional quantitative model. In addition to the population development, which often indicates a reurbanisation or suburbanisation trend, it includes the labour market and land consumption. Going beyond city-hinterland relations, it is investigated whether the surrounding areas of large cities show a development according to the ideal of decentralised concentration - in particular a concentration on medium-sized cities - or whether the development is more dispersely spread over smaller towns and municipalities. The calculations lead to a suburbanisation index, which can be periodically produced every few years (monitoring). Beyond a perspective mainly orientated towards the population development, the suburbanisation idea is improved and completed. Comparing the various dimensions (population, labour market, land consumption) generates partly controversial results

Measuring polycentric structures of megaregions in China: linking morphological and functional dimensions

The idea of megaregions, which focuses on polycentricity, competitiveness, and integration attracts much attention in research and policy. China has used megaregions as a normative governance framework that leverages polycentric regional development for balancing economic competitiveness and spatial development. This paper explores to what extent these megaregions actually reveal polycentric versus monocentric structures. The analysis demonstrates a divergence between the morphological and functional organization of China’s megaregions. Five types of megaregions are identified as per the relationships between the morphological and functional dimensions. Functionally, the Pearl River Delta, Shandong Peninsula, and Yangtze River Delta are among the most polycentric megaregions. The majority of others, even where morphologically polycentric, do not exhibit high degrees of functional polycentricity. The study demonstrates a problematic nature of megaregions as a governance agenda for regional polycentricity. It argues that if China genuinely wants to achieve greater levels of polycentricity and spatial cohesion, differentiated policies should be implemented for megaregions

Urban nighttime leisure space mapping with nighttime light images and POI data

Urban nighttime leisure spaces (UNLSs), important urban sites of nighttime economic activity, have created enormous economic and social benefits. Both the physical features (e.g., location, shape, and area) and the social functions (e.g., commercial streets, office buildings, and entertainment venues) of UNLSs are important in UNLS mapping. However, most studies rely solely on census data or nighttime light (NTL) images to map the physical features of UNLSs, which limits UNLS mapping, and few studies perform UNLS mapping from a social function perspective. Point-of-interest (POI) data, which can reflect social activity functions, are needed. As a result, a novel methodological UNLS mapping framework, that integrates NTL images and POI data is required. Consequently, we first extracted high-NTL intensity and high-POI density areas from composite data as areas with high nightlife activity levels. Then, the POI data were analyzed to identify the social functions of leisure spaces revealing that nighttime leisure activities are not abundant in Beijing overall, the total UNLS area in Beijing is 31.08 km(2), which accounts for only 0.2% of the total area of Beijing. In addition, the nightlife activities in the central urban area are more abundant than those in the suburbs. The main urban area has the largest UNLS area. Compared with the nightlife landmarks in Beijing established by the government, our results provide more details on the spatial pattern of nighttime leisure activities throughout the city. Our study aims to provide new insights into how multisource data can be leveraged for UNLS mapping to enable researchers to broaden their study scope. This investigation can also help government departments better understand the local nightlife situation to rationally formulate planning and adjustment measures

Graph convolutional networks for street network analysis with a case study of urban polycentricity in Chinese cities

Graph theory effectively explains urban structures via street–street connectivity. However, systematic comparisons of street structures across cities remain challenging. This study employs graph convolutional networks (GCNs) to analyze street network structures. A two-branch GCN was used as the backbone to extract comparable features among street networks. The proposed approach was used to examine the structures of different urban road networks in a case study of polycentricity prediction across 298 Chinese cities. The model transformed approximately 4.5-million street segments into natural streets to create urban street graphs, which were subsequently analyzed to extract local and global embeddings. The extracted embeddings – with a portion labeled with a known urban polycentricity score – were used to predict the score for each city through a single-layer perceptron (SLP) model. Our results show consistency between the predicted polycentricity scores based on the derived street embeddings and those based on the population. Thus, the proposed GCN-based method can effectively predict the complexity and interconnection of street networks in different cities. This innovative integration of GCNs into urban studies demonstrates that deep learning techniques can analyze and comprehend the intricate patterns of street networks on a large scale

EO + morphometrics : understanding cities through urban morphology at large scale

Earth Observation (EO)-based mapping of cities has great potential to detect patterns beyond the physical ones. However, EO combined with the surge of machine learning techniques to map non-physical, such as socioeconomic, aspects directly, goes to the expense of reproducibility and interpretability, hence scientific validity. In this paper, we suggest shifting the focus from the direct detection of socioeconomic status from raw images through image features, to the mapping of interpretable urban morphology of basic urban elements as an intermediate step, to which socioeconomic patterns can then be related. This shift is profound, in that, rather than abstract image features, it allows to capture the morphology of real urban objects, such as buildings and streets, and use this to then interpret other patterns, including socioeconomic ones. Because socioeconomic patterns are not derived from raw image data, the mapping of these patterns is less data demanding and more replicable. Specifically, we propose a 2-step approach: (1) extraction of fundamental urban elements from satellite imagery, and (2) derivation of meaningful urban morphological patterns from the extracted elements. We refer to this 2-step approach as “EO + Morphometrics”. Technically, EO consists of applying deep learning through a reengineered U-Net shaped convolutional neural network to publicly accessible Google Earth imagery for building extraction. Methods of urban morphometrics are then applied to these buildings to compute semantically explicit and interpretable metrics of urban form. Finally, clustering is applied to these metrics to obtain morphological patterns, or urban types. The “EO + Morphometrics” approach is applied to the city of Nairobi, Kenya, where 15 different urban types are identified. To test whether this outcome meaningfully describes current urbanization patterns, we verified whether selected types matched locally designated informal settlements. We observe that four urban types, characterized by compact and organic urban form, were recurrent in such settlements. The proposed "EO + Morphometrics" approach paves the way for the large-scale identification of interpretable urban form patterns and study of associated dynamics across any region in the world