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Relevant bibliographies by topics / Urban Index (UI)

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Journal articles

Academic literature on the topic 'Urban Index (UI)'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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Journal articles on the topic "Urban Index (UI)"

1

Muhaimin, M., D. Fitriani, S. Adyatma, and D. Arisanty. "mapping build-up area density using normalized difference built-up index (ndbi) and urban index (ui) wetland in the city banjarmasin." IOP Conference Series: Earth and Environmental Science 1089, no. 1 (2022): 012036. http://dx.doi.org/10.1088/1755-1315/1089/1/012036.

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Abstract Density and undeveloped land can be identified using digital transformation through remote sensing data. This study aims to map the distribution of building densities with the Normalized Difference Built-Up Index (NDBI) and Urban Index (UI), analyze the comparison of building densities of the NDBI and UI methods, and analyze the relationship between the transformation of NDBI and UI on building densities in Banjarmasin City. The data used to obtain building density image Landsat 8 Oli Tirs. The method used to separate the built area and non-building area using digital classification.

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2

Guha, Subhanil, Himanshu Govil, and Neelay Srivastava. "An Analytical Study on the Relationship of Land Surface Temperature with Normalized Difference Built-up Index, Urban Index, and Built-up Index." Ekológia (Bratislava) 44, no. 1 (2025): 69–80. https://doi.org/10.2478/eko-2025-0009.

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Abstract The present study examines three urban indices: the normalized difference built-up index (NDBI), the urban index (UI), and the built-up index (BUI). It analyzes their relationship with land surface temperature (LST) using 12 Landsat 8 datasets collected during the summer season from 2013 to 2024 in Raipur, a tropical city in India. The results indicate that the mean LST of the city exceeded 40 °C in 2013, but this temperature has been moderated in subsequent years due to the implementation of ecofriendly initiatives. Among the three indices, NDBI shows the least variability. All three

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3

Cigerci, Hazal, Filiz Bektas Balcik, Aliihsan Sekertekin, and Ceyhan Kahya. "Unveiling Istanbul’s City Dynamics: Spatiotemporal Hotspot Analysis of Vegetation, Settlement, and Surface Urban Heat Islands." Sustainability 16, no. 14 (2024): 5981. http://dx.doi.org/10.3390/su16145981.

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Investigation of cities’ spatiotemporal dynamics, including vegetation and urban areas, is of utmost importance for understanding ecological balance, urban planning, and sustainable development. This study investigated the dynamic interactions between vegetation, settlement patterns, and surface urban heat islands (SUHIs) in Istanbul using spatiotemporal hotspot analysis. Utilizing Landsat satellite imagery, we applied the Getis-Ord Gi* statistic to analyze Land Surface Temperature (LST), Urban Index (UI), and Normalized Difference Vegetation Index (NDVI) across the city. Using satellite image

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Bashit, Nurhadi, Yudo Prasetyo, and Abdi Sukmono. "KAJIAN PERKEMBANGAN LAHAN TERBANGUN KOTA PEKALONGAN MENGGUNAKAN METODE URBAN INDEX (UI)." Elipsoida : Jurnal Geodesi dan Geomatika 2, no. 02 (2019): 12–18. http://dx.doi.org/10.14710/elipsoida.2019.6440.

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Indonesia merupakan negara berkembang yang mengalami peningkatan pembangunan di wilayah perkotaan sehingga dapat dilihat berdasarkan perubahan fisik perkotaan. Perkembangan pembangunan dapat menyebabkan perubahan penggunaan lahan dari lahan kosong menjadi lahan terbangun. Perubahan lahan terbangun merupakan hal yang umum terjadi di perkotaan karena pertumbuhan jumlah penduduk. Peningkatan lahan terbangun dan menurunnya lahan terbuka hijau dapat menyebabkan dampak perubahan fungsi lahan. Perubahan fungsi lahan memberikan dampak pada perubahan tatanan lingkungan berupa menurunnya kualitas lingku

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5

Ghosh, Debashish Kumar, Anukul Ch Mandal, Raja Majumder, Poly Patra, and Gouri Sankar Bhunia. "Analysis for Mapping of Built-Up Area Using Remotely Sensed Indices – A Case Study of Rajarhat Block in Barasat Sadar Sub-Division in West Bengal (India)." Journal of Landscape Ecology 11, no. 2 (2018): 67–76. http://dx.doi.org/10.2478/jlecol-2018-0007.

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Abstract Present study investigated mapping and monitoring urban land areas from Landsat8 satellite data using remotely sensed indices. The normalized difference built-up index (NDBI), Enhanced Built-Up and Bareness Index (EBBI), Index-based built-up index (IBI), urban index (UI), normalized difference bareness index (NDBaI) were used to extract the built-up area. The NDBI was more effective at discriminating built-up areas and at increasing accuracy (overall accuracy of 76.45 % and kappa accuracy of 57 %) of the built-up density percentage than other remotely sensed indices. Evidence on built

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6

Pang, Haiyu, Tangdi Lin, Qing Liu, et al. "Prevalence of urinary incontinence among nulliparous women and its association with underweight body mass index: a secondary analysis of a nationwide cross-sectional study in China." BMJ Open 15, no. 5 (2025): e097807. https://doi.org/10.1136/bmjopen-2024-097807.

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Objectives To estimate the prevalence of urinary incontinence (UI) and its subtypes among nulliparous Chinese women with associated risk factors. The prevalence of UI among those living in urban or rural communities was also analysed with potential risk factors. Design This is a secondary analysis of epidemiological survey data on UI in Chinese women. The original study was designed as a nationwide cross-sectional study involving 56 460 adult women conducted from October 2019 to December 2021. Setting Seven geographic regions of China. Participants Nulliparous women who were aged ≥20 years old

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7

Suharyadi, R., Deha Agus Umarhadi, Disyacitta Awanda, and Wirastuti Widyatmanti. "Exploring Built-Up Indices and Machine Learning Regressions for Multi-Temporal Building Density Monitoring Based on Landsat Series." Sensors 22, no. 13 (2022): 4716. http://dx.doi.org/10.3390/s22134716.

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Uncontrolled built-up area expansion and building densification could bring some detrimental problems in social and economic aspects such as social inequality, urban heat islands, and disturbance in urban environments. This study monitored multi-decadal building density (1991–2019) in the Yogyakarta urban area, Indonesia consisting of two stages, i.e., built-up area classification and building density estimation, therefore, both built-up expansion and the densification were quantified. Multi sensors of the Landsat series including Landsat 5, 7, and 8 were utilized with some prior corrections t

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8

Chen, Min, Yu Jiang, Endong Wang, Yi Wang, and Jun Zhang. "Measuring Urban Infrastructure Resilience via Pressure-State-Response Framework in Four Chinese Municipalities." Applied Sciences 12, no. 6 (2022): 2819. http://dx.doi.org/10.3390/app12062819.

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Urban infrastructure (UI), subject to ever-increasing stresses from artificial activities of human beings and natural disasters due to climate change, assumes a key role in modern cities for maintaining their functional operations. Therefore, understanding UI resilience turns essential. Based on the Pressure-State-Response (PSR) model, this paper built a comprehensive evaluation index system for urban infrastructure resilience evaluation. Four municipalities, including Beijing, Tianjin, Shanghai, and Chongqing in China, were selected for the case study, given their specific significance in ter

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9

Niklas, Ulrich, Sascha von Behren, Tamer Soylu, Johanna Kopp, Bastian Chlond, and Peter Vortisch. "Spatial Factor—Using a Random Forest Classification Model to Measure an Internationally Comparable Urbanity Index." Urban Science 4, no. 3 (2020): 36. http://dx.doi.org/10.3390/urbansci4030036.

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Travel behavior can be determined by its spatial context. If there are many shops and restaurants in close proximity, various activities can be done by walking or cycling, and a car is not needed. It is also more difficult (e.g., parking space, traffic jams) to use a car in high-density areas. Overall, travel behavior and dependencies on travel behavior are influenced by urbanity. These relationships have so far only been examined very selectively (e.g., at city level) and not in international comparison. In this study we define an Urbanity Index (UI) at zip code level, which considers factors

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10

Guha, Subhanil, and Himanshu Govil. "An analytical study on urban indices and land surface temperature." Journal of Environmental Engineering and Landscape Management 32, no. 3 (2024): 231–40. http://dx.doi.org/10.3846/jeelm.2024.21835.

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Any urban landscape needs to investigate the rising trend of land surface temperature (LST) with its surface materials. The present study analyzes the relationship of LST with three urban indices namely normalized difference built-up index (NDBI), urban index (UI), and built-up index (BUI) (by Pearson correlation coefficient method) using nine Landsat 8 OLI and TIRS data of May from 2013 to 2021 in a tropical Indian city, Raipur. Results show that the mean LST of the city was above 40 oC in 2013 but it is controlled in successive years by executing some eco-friendly activities. All the indices

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