Urban Hotspots Research Articles

Urban Network Hotspot Consumption Space Evolution Mechanism and Crowd Differentiation Analysis: A Case Study of Lushan South Road in Changsha City

With the prosperity of the tourism industry, foreign tourists and local university students have become the main users of the university town space. The two groups have a high degree of behavioral overlap but differ in motivation due to different psychological needs. Research on urban network hotspot space needs more exploration of small-scale road sections and coverage of spatial boundary perception, social media, and other factors in space formation and evolution mechanisms. Therefore, this study will take the consumption space with the strongest interweaving of the two groups as the starting point to explore the countermeasures for spatial boundary perception with the theoretical support of scene theory. It was found that various factors such as social media, tourist experience-oriented consumption psychology, and the perception of spatial boundaries by both tourists and students influenced the drastic change in urban hotspot consumption space. This study provides some reference for understanding and guiding the development of such areas.

TOWARDS LARGE-SCALE BUILDING ATTRIBUTE MAPPING USING CROWDSOURCED IMAGES: SCENE TEXT RECOGNITION ON FLICKR AND PROBLEMS TO BE SOLVED

Abstract. Crowdsourced platforms provide huge amounts of street-view images that contain valuable building information. This work addresses the challenges in applying Scene Text Recognition (STR) in crowdsourced street-view images for building attribute mapping. We use Flickr images, particularly examining texts on building facades. A Berlin Flickr dataset is created, and pre-trained STR models are used for text detection and recognition. Manual checking on a subset of STR-recognized images demonstrates high accuracy. We examined the correlation between STR results and building functions, and analysed instances where texts were recognized on residential buildings but not on commercial ones. Further investigation revealed significant challenges associated with this task, including small text regions in street-view images, the absence of ground truth labels, and mismatches in buildings in Flickr images and building footprints in OpenStreetMap (OSM). To develop city-wide mapping beyond urban hotspot locations, we suggest differentiating the scenarios where STR proves effective while developing appropriate algorithms or bringing in additional data for handling other cases. Furthermore, interdisciplinary collaboration should be undertaken to understand the motivation behind building photography and labeling. The STR-on-Flickr results are publicly available at https://github.com/ya0-sun/STR-Berlin.

Oxidative potential in rural, suburban and city centre atmospheric environments in central Europe

Abstract. Oxidative potential (OP) is an emerging health-related metric which integrates several physicochemical properties of particulate matter (PM) that are involved in the pathogenesis of the diseases resulting from exposure to PM. Daily PM2.5-fraction aerosol samples collected in the rural background of the Carpathian Basin and in the suburban area and centre of its largest city of Budapest in each season over 1 year were utilised to study the OP at the related locations for the first time. The samples were analysed for particulate matter mass, main carbonaceous species, levoglucosan and 20 chemical elements. The resulting data sets were subjected to positive matrix factorisation to derive the main aerosol sources. Biomass burning (BB), suspended dust, road traffic, oil combustion mixed with coal combustion and long-range transport, vehicle metal wear, and mixed industrial sources were identified. The OP of the sample extracts in simulated lung fluid was determined by ascorbic acid (AA) and dithiothreitol (DTT) assays. The comparison of the OP data sets revealed some differences in the sensitivities of the assays. In the heating period, both the OP and PM mass levels were higher than in spring and summer, but there was a clear misalignment between them. In addition, the heating period : non-heating period OP ratios in the urban locations were larger than for the rural background by factors of 2–4. The OP data sets were attributed to the main aerosol sources using multiple linear regression with the weighted least squares approach. The OP was unambiguously dominated by BB at all sampling locations in winter and autumn. The joint effects of motor vehicles involving the road traffic and vehicle metal wear played the most important role in summer and spring, with considerable contributions from oil combustion and resuspended dust. In winter, there is temporal coincidence between the most severe daily PM health limit exceedances in the whole Carpathian Basin and the chemical PM composition causing larger OP. Similarly, in spring and summer, there is a spatial coincidence in Budapest between the urban hotspots of OP-active aerosol constituents from traffic and the high population density in central quarters. These features offer possibilities for more efficient season-specific air quality regulations focusing on well-selected aerosol sources or experimentally determined OP, rather than on PM mass in general.

Global drought risk in cities: present and future urban hotspots

Hydrological droughts pose a persistent threat for cities and are increasingly studied. However, this is rarely within a large-scale context, complicating comparisons between cities and potentially hampering the most efficient allocation of resources in terms of drought risk adaptation and mitigation. Here, we investigate global urban hydrological drought risk for 264 urban agglomerations across all continents for both the present time and future projections. To derive risk profiles for each agglomeration, we include components of: drought hazard (drought volume focusing on surface water deficits), exposure (urban population), vulnerability (multivariate vulnerability index), and cost (replacement of freshwater expenses). These components are dynamic in time, except for vulnerability. Most agglomerations are projected to experience an increase in drought hazard, exposure, and cost by 2050, with the most notable current and future hotspot being northern South Asia (India & Pakistan). Also, the number of agglomerations with high risk increases, whereas the number with lower risk decreases, indicating that high urban drought risk is increasing in scale over time. Our results enable a better targeting of those agglomerations that need most urgent attention in terms of drought risk solutions. It can also be used to identify agglomerations with similar drought risk profiles that could be studied in conjunction and may benefit from cooperative drought risk management strategies.

Identifying and monitoring the Urban Heat Island in the compact Mediterranean city using satellite imagery and in-situ measurement data

The combination of the Urban Heat Island (UHI) phenomenon and the increased frequency and intensity of summer heat waves causes increased energy demand and consumption for air cooling and augmented thermal discomfort and heat-related morbidity and mortality in urban areas. This circumstance is particularly problematic in areas where a rise in the mean air temperature can be already observed, like the Mediterranean Basin. Although the UHI effect is a well-known phenomenon, a detailed investigation of the relationship between urban morphology and temperature distributions in actual compact Mediterranean cities needs further investigation. Therefore, the present study is conducted by employing satellite imagery and in-situ measurements to monitor the UHI phenomenon and identify urban hotspots in several cities in Italy. It should be noted that the selected cities differ in latitude, climate, size, altitude, and location, allowing the investigation of the relationship between urban morphologies, air, and surface temperature in different contexts. This paper reports the methodology developed to carry out ongoing research and some preliminary results.

Mapping of Thermal Indices Using an Automated Landsat 8-based-ArcGIS Model: A Case Study in Alexandria City, Egypt

Urbanization is a contributing factor to global warming, as asphalt, concrete, and other light-absorbing materials replace vegetated areas, causing an increase in Land Surface Temperature (LST) and creating Surface Urban Heat Islands (SUHI). Although thermal satellite imagery has been a powerful tool in mapping LST and SUHI spatio-temporal changes, the number of studies in Africa, including Egypt, remains limited. Thus, in this research, an automated model was developed in ArcGIS and used to map LST and SUHI and detect Urban Hot Spots (UHS) in Alexandria city, Egypt, using Landsat 8 time series (2013 to 2021). The results revealed an increase of 38.35% in urban areas and a decrease of 50.79% in agricultural areas, a change that was demonstrated by a decline in the Normalized Difference Vegetation Index (NDVI) from 0.84 in 2013 to 0.53 in 2021. Correspondingly, LST and SUHI displayed an increasing pattern, with the highest recorded values observed in 2021. Thus, this study showed the negative impact of urbanization on Alexandria city’s temperature – a city that is already facing a climate catastrophe because of the sea level rise resulting from climate change. Furthermore, the developed estimation model can be similarly useful for climate change researchers and decision makers.

Optimal Route Determination Automation System for Covid-19 Medical Waste Disposal Based on 3D Building Modeling

Urbanization is a contributing factor to global warming, as asphalt, concrete, and other light-absorbing materials replace vegetated areas, causing an increase in Land Surface Temperature (LST) and creating Surface Urban Heat Islands (SUHI). Although thermal satellite imagery has been a powerful tool in mapping LST and SUHI spatio-temporal changes, the number of studies in Africa, including Egypt, remains limited. Thus, in this research, an automated model was developed in ArcGIS and used to map LST and SUHI and detect Urban Hot Spots (UHS) in Alexandria city, Egypt, using Landsat 8 time series (2013 to 2021). The results revealed an increase of 41.31% in urban areas and a decrease of 49.51% in agricultural areas, a change that was demonstrated by a decline in the Normalized Difference Vegetation Index (NDVI) from 0.84 in 2013 to 0.53 in 2021. Correspondingly, LST and SUHI displayed an increasing pattern, with the highest recorded values observed in 2021. Thus, this study showed the negative impact of urbanization on Alexandria city’s temperature – a city that is already facing a climate catastrophe because of the sea level rise resulting from climate change. Furthermore, the developed estimation model can be similarly useful for climate change researchers and decision makers.

CPTED Principles and Preventing Crimes: The Cases from Shiraz City, Iran

The current paper investigates the role of CPTED (crime prevention through environmental design) principles in preventing crimes in urban hot spots. The questionnaire survey gathers the opinions of 400 participants randomly. Moreover, the study employs an observational study to analyze the actual condition of the areas. Analysis shows territorial reinforcement and surveillance most significantly impact crime prevention, while maintenance imposes the lowest impact in urban contexts. The findings imply that enhancing the cultural bases of the residents outweighs improving the physical appearance of the environment in preventing crimes. Overall, the findings offer practical suggestions for future planning of urban hot spots that contribute to environmental quality.

Spatio-temporal variability of the earth’s surface temperature and the changes in land user/land cover: implications for sustainable tourism development

ABSTRACT Tourism both contributes to environmental change and is, in turn, affected by such change. One area that is of considerable significance is tourism’s contribution to land use change and its subsequent impacts. Land use change, such as tourism urbanization, together with global heating leads to an increase in surface temperature and deterioration in environmental quality and human comfort levels. Understanding the implications of tourism-related land use change is therefore of considerable importance for climate adaptation. Using Landsat satellite images, the evolution of land users/covers and the temperature of the land surface between 1985 and 2020 were studied in Cyprus. The analysis of the data confirms that tourism development activities have influenced the increase in urban hotspots. This destination has experienced a 6.2°C increase in average temperature and 168.8% in the urban hotspot. Based on the findings the study highlights the implications for sustainable tourism development and potential means of mitigation.

One Health Approach to Leptospirosis: Human-Dog Seroprevalence Associated to Socioeconomic and Environmental Risk Factors in Brazil over a 20-Year Period (2001-2020).

Despite being considered a neglected, re-emerging and the most widespread zoonotic disease worldwide, human-dog leptospirosis has not been subjected to One Health approach, and neither were its socioeconomic and environmental risk factors, as well as concomitant spatial analysis over time. Accordingly, notified human leptospirosis cases, incidence rate and urban hotspot areas, in addition to a systematic review of dog leptospirosis cases, were performed nationwide from 2001 to 2020 in Brazil. Data on Gross Domestic Product (GDP), flooding and study areas were also assessed and tabulated. Human-dog leptospirosis cases were simultaneously mapped with overlapping flooding areas, along with the main circulant serovars. Comparative outcome has shown that dogs may be exposed similarly to humans, becoming important sentinels and/or reservoirs for human leptospirosis in larger geographic areas. Moreover, the study herein can help in the decision and implementation of public policies in Brazil and may serve as a model for other tropical countries worldwide.

İzmir'de kentleşmenin neden olduğu kent ekosistemi ve yerel iklim değişikliklerinin uzun süreli KIA oluşumu açısından STG yöntemiyle değerlendirilmesi

Even if urbanization offers various opportunities to people living in todays world. It also comes with some side effects such as worsening climate conditions by creating thermal pollution due to certain urban activities, sectoral urban designs and consequent patterns in cities. In local sense, the old climatic conditions beforete the change because of urbanization in rural areas can be called natural when they are compared with new conditions deteriorated by widespread urbanization. Thus, thermal pollution changes city’s local climate over time and negatively affects city’s resilience. Here in this research, it is determined themperature related local climate variation caused by specific city activities in the city of Izmir by analysing time series thermal data distribution over the entire city over a certain period of time and for this analyse even a novel approach is introduced and suggested which is a Simulated Single Image (SSI) method based on Simulated Single Data (SSD) statistical analyze. The method uses not only trend or average values of time series data as being as usual but it uses both and also standart deviation of the data to support a single output from the time series data analyse. Thus, outputs were obtained as single images from the the LANDSAT time series data to represent where generally Urban Hot Spots (UHS) appear and Urban Heat Islands (UHI) develop in the city. Stereo representation of the study region is also used to visually examine the topographical effect on UHI distribution in the city. Izmir which is the third mostly populated city of Turkey located on the Izmir Gulf of Egean Sea is chosen as study area and the study clearly demonstrated that industrial regions and roads with large surfaces, bare lands with sparse bushes, empty or sparse grassy urban lands and more significantly the urban land parts faced to certain directions are the main urban land cover and structure types contributing UHSs to appear and UHI developments in the city.

Significant urban hotspots of atmospheric trace element deposition and potential effects on urban soil pollution in China

Rapid urbanization has profoundly altered the spatial patterns of multiple element cycles. Whether and how urbanization shapes the spatial patterns of atmospheric trace element deposition remains, however, poorly understood. Using a newly compiled database on bulk deposition of eight trace elements (i.e., Cu, Ni, Zn, As, Cd, Cr, Hg and Pb) in China, we assessed the urban imprints on the spatial patterns of trace element deposition. Bulk deposition of the eight trace elements all showed a significant increase with closer distance to the nearest large cities, while the urban effect was also mediated by point emission sources and precipitation. We further compiled a database of urban topsoil (0–10 cm) concentrations of the eight trace elements and found that urban soil quality standards were exceeded in 80% of the studied cities for Cr, 49% for As, and less than 25% for other trace elements, respectively. The urban topsoil concentrations of six trace elements (except As and Hg) showed no significant correlations with their background values for natural soils, while we found a significant correlation between bulk deposition and urban topsoil concentrations of trace elements corrected by background values. We also demonstrated that current levels of trace element deposition would substantially increase urban soil pollution over the coming decades. Our findings confirm the occurrence of urban hotspots of trace element deposition and their impact on soil pollution and highlight a need of emission control of trace elements for safety urban soil quality.

Spatial hotspot detection in the presence of global spatial autocorrelation

The presence of global spatial autocorrelation usually leads to the spurious identification of spatial hotspots and hinders the identification of local hotspots. Despite the use of statistical methods to address global spatial autocorrelation in spatial hotspot detection, accurately modeling global spatial autocorrelation structure without the stationarity assumption of spatial processes is difficult. To overcome this challenge, we fitted the global spatial autocorrelation structure from a geometric perspective and identified the optimal global spatial autocorrelation structure by analyzing the variances in spatial data. Hotspots were detected from the residuals obtained by removing the global spatial autocorrelation structure from the original dataset. We upgraded a weighted moving average method based on binomial coefficients (Yang Chizhong filtering) to fit the global spatial autocorrelation structure for field-like geographic phenomena. A variance decay indicator, based on the variance in the original and filtered data, was used to identify the optimal global spatial autocorrelation structure. Yang Chizhong filtering does not require a spatial stationarity assumption and can preserve local autocorrelation structures in the residuals as much as possible. Experimental results showed that hotspot detection methods combined with Yang Chizhong filtering can effectively reduce type-I and -II errors in the results and discover implicit and valuable urban hotspots.

The geographical dynamics of global R&D collaboration networks in robotics: Evidence from co-patenting activities across urban areas worldwide.

The focus of this study is on the geography of robotics Research and Development (R&D) activities. The objectives are, first, to identify hotspots in robotics R&D worldwide, and second, to characterise structures and dynamics of global robotics R&D collaboration networks through detailed geographical lenses of global urban areas. We use patents as marker for R&D activities, and accordingly focus on technologically oriented R&D, drawing on information from patents applied for between 2002 and 2016. We employ an appropriate search strategy to identify relevant robotics patents based on detailed levels of the Cooperative Patent Classification (CPC) and assign patents to more than 900 global urban areas based on the inventor addresses. The co-patent networks are examined from a Social Network Analysis (SNA) perspective by means of robotics co-patents, contributing to a global network where urban areas are the nodes inter-linked by joint inventive activities recorded in robotics patents. Global SNA measures illustrate structures and dynamics of the network as a whole, while local measures indicate the specific positioning and roles of urban areas in the network. The results are original in characterising the global spatial emergence of this generic new industry, highlighting prominent urban hotspots in terms of specialisation in robotics R&D, pointing to a global shift reflected by the increasing role of emerging economies, in particular China. The global robotics R&D has grown significantly both in total patenting and also in terms of R&D collaboration activities between urban areas. Also, for the networks, growth is not equally distributed, but is rather characterised by significant spatial shifts, both in terms of cities declining or climbing up the specialisation ranking, but even more in terms of the spatial network structure.

Modeling the Impact and Risk Assessment of Urbanization on Urban Heat Island and Thermal Comfort Level of Beijing City, China (2005–2020)

Rapid urbanization poses a threat to various ecosystem services. Beijing has undergone extensive infrastructure development in recent years. The study aims to extract land surface temperature (LST) and land use cover (LUC) data from satellite imagery, identify urban heat island (UHI) areas in Beijing, and determine the correlation between LST, LUC, NDVI, and BUI. It will also investigate the relationship between UHI and built/unbuilt areas, evaluate thermal comfort in Beijing using UTFVI, and assess the ecological quality of different land use types using the Ecological Evaluation Index (EEI). The results can inform urban planning and management in rapidly urbanizing and climate-changing regions. Changes in LUC and other activities affect the distribution of LST. For the study years (2005–2020), the estimated mean LST in Beijing was 24.72 °C, 27.07 °C, 26.22 °C, and 27.03 °C, respectively. A significant positive correlation (r = 0.96 p > 0.005) was found between LST and urban areas with other infrastructures. Geographically weighted regression (GWR) outperformed with Adj R2 > 0.74, suggesting that the extent of an urban heat island (UHI) is strongly dependent on the settlements, LUC composition, size, and terrain of surrounding communities. Urban hotspots in the city were identified and validated using Google Earth imagery. The Ecological Evaluation Index (EEI) value was relatively low compared to other ecosystem-related units. EEI showed a continuous increase of six percent in the most negative categories, indicating an unstable environment. This study concludes that urbanization affects the city’s environment, and study findings would help to regulate the urban ecosystem in Beijing.

Spatiotemporal analysis of the surface urban heat island (SUHI), air pollution and disease pattern: an applied study on the city of Granada (Spain)

There is worldwide concern about how climate change —which involves rising temperatures— may increase the risk of contracting and developing diseases, reducing the quality of life. This study provides new research that takes into account parameters such as land surface temperature (LST), surface urban heat island (SUHI), urban hotspot (UHS), air pollution (SO2, NO2, CO, O3 and aerosols), the normalized difference vegetation index (NDVI), the normalized difference building index (NDBI) and the proportion of vegetation (PV) that allows evaluating environmental quality and establishes mitigation measures in future urban developments that could improve the quality of life of a given population. With the help of Sentinel 3 and 5P satellite images, we studied these variables in the context of Granada (Spain) during the year 2021 to assess how they may affect the risk of developing diseases (stomach, colorectal, lung, prostate and bladder cancer, dementia, cerebrovascular disease, liver disease and suicide). The results, corroborated by the statistical analysis using the Data Panel technique, indicate that the variables LST, SUHI and daytime UHS, NO2, SO2 and NDBI have important positive correlations above 99% (p value: 0.000) with an excess risk of developing these diseases. Hence, the importance of this study for the formulation of healthy policies in cities and future research that minimizes the excess risk of diseases.

Land Surface Temperature Relationship with the Land Use/Land Cover Indices Leading to Thermal Field Variation in the Turkish Republic of Northern Cyprus

The increase in the Land Surface Temperature (LST) caused by global warming and extreme weather events is significantly increasing Urban Hot Spots (UHS) while impacting the environmental quality within urban areas. In this research, with the help of Landsat 5, 7, and 8 satellite images, the evolution of Land Use/Cover (LULC), LST, and Surface Urban Heat Island (SUHI) in the Turkish Republic of Northern Cyprus (TRNC) between the years 1985 and 2020 is examined. The main aim is to evaluate how these variables, together with the Normalized Difference Built-up Index (NDBI), Urban Index (UI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Built Index, and Proportion Vegetation (PV) have influenced the variability of the UHS and the level of thermal comfort according to the Thermal Field Variance Index (UTFVI). The results report significant average rises of the variables: LST (6.62 °C), SUHI (0.97 °C), UHS (8.61%) of the UI and NDBI indices and class 6 of UTFVI (88.53%) that are related to the variability of the LULC corroborated by statistical analysis. Our results provide valuable information on the future development of urban areas in Northern Cyprus to make them more resilient and sustainable to rising temperatures.

NIGHTTIME LIGHT: A POTENTIAL PROXY FOR LOCAL NOCTURNAL URBAN HEAT ISLAND INTENSITY IN SEOUL

ABSTRACT Nighttime light (NTL) imagery provides valuable information for monitoring urban development, human activity, and, consequently, urban thermal environments. However, only a few studies have attempted to directly draw linkages between NTL and urban heat island (UHI) formation. Establishing associations between NTL and UHI indicators could potentially ease the process of identifying heightened thermal environments and thus pinpointing urban hotspots in need of policy interventions and strategic mitigation measures. In the present study, through clustering and correlation analysis, we study ties between NTL, surface urban heat island intensity (SUHII) and atmospheric urban heat island intensity (AUHII) in Seoul. NTL data was derived from the Suomi National Polar-Orbiting Partnership-Visible and Infrared imager/Radiometer Suite (SNPP-VIIRS), while SUHII data was obtained from the moderate resolution imaging spectroradiometer (MODIS) satellite instrument. Additionally, AUHII was computed from archived weather data collected from multiple automatic weather stations (AWSs) in Seoul. Our results show that NTL is highly positively correlated with SUHII (R > 0.86, p < 0.05) and AUHII (R > 0.68, p < 0.05). The findings highlight the potential usage of NTL as a proxy for UHI formation, simplifying the process of identifying heightened thermal areas in Seoul that may require rapid policy intervention.

Urban hotspot forecasting via automated spatio-temporal information fusion

Urban hotspot forecasting is one of the most important tasks for resource scheduling and security in future smart cities. Most previous works employed fixed neural architectures based on many complicated spatial and temporal learning modules. However, designing appropriate neural architectures is challenging for urban hotspot forecasting. One reason is that there is currently no adequate support system for how to fuse multi-scale spatio-temporal information rationally by integrating different spatial and temporal learning modules. Another one is that the empirical fixed neural architecture is difficult to adapt to different data scenarios from different domains or cities. To address the above problems, we propose a novel framework based on neural architecture search for urban hotspot forecasting, namely Automated Spatio-Temporal Information Fusion Neural Network (ASTIF-Net). In the search space of our ASTIF-Net, normal convolution and graph convolution operations are adopted to capture spatial geographic neighborhood dependencies and spatial semantic neighborhood dependencies, and different types of temporal convolution operations are adopted to capture short-term and long-term temporal dependencies. In addition to combining spatio-temporal learning operations from different scales, ASTIF-Net can also search appropriate fusion methods for aggregating multi-scale spatio-temporal hidden information. We conduct extensive experiments to evaluate ASTIF-Net on three real-world urban hotspot datasets from different domains to demonstrate that our proposed model can obtain effective neural architectures and achieve superior performance (about 5%∼10% improvements) compared with the existing state-of-art baselines.

Detecting Multi-Density Urban Hotspots in a Smart City: Approaches, Challenges and Applications

Leveraged by a large-scale diffusion of sensing networks and scanning devices in modern cities, huge volumes of geo-referenced urban data are collected every day. Such an amount of information is analyzed to discover data-driven models, which can be exploited to tackle the major issues that cities face, including air pollution, virus diffusion, human mobility, crime forecasting, traffic flows, etc. In particular, the detection of city hotspots is de facto a valuable organization technique for framing detailed knowledge of a metropolitan area, providing high-level summaries for spatial datasets, which are a valuable support for planners, scientists, and policymakers. However, while classic density-based clustering algorithms show to be suitable for discovering hotspots characterized by homogeneous density, their application on multi-density data can produce inaccurate results. In fact, a proper threshold setting is very difficult when clusters in different regions have considerably different densities, or clusters with different density levels are nested. For such a reason, since metropolitan cities are heavily characterized by variable densities, multi-density clustering seems to be more appropriate for discovering city hotspots. Indeed, such algorithms rely on multiple minimum threshold values and are able to detect multiple pattern distributions of different densities, aiming at distinguishing between several density regions, which may or may not be nested and are generally of a non-convex shape. This paper discusses the research issues and challenges for analyzing urban data, aimed at discovering multi-density hotspots in urban areas. In particular, the study compares the four approaches (DBSCAN, OPTICS-xi, HDBSCAN, and CHD) proposed in the literature for clustering urban data and analyzes their performance on both state-of-the-art and real-world datasets. Experimental results show that multi-density clustering algorithms generally achieve better results on urban data than classic density-based algorithms.