OpenStreetMap Research Articles

Analysis and optimization of 15-minute community life circle based on supply and demand matching: A case study of Shanghai.

The 15-minute community life circle (15min-CLC) strategy is one of Shanghai's important methods for building a global city and facing a society with a more diverse population structure in the future. In the existing research, the balance between the construction of the life circle and the needs of the people in the life circle still needs to be further fulfilled. This paper is based on the city's multi-source large data set including 2018 AutoNavi POI (Point of Interests), OSM (OpenStreetMap) road network data and LandScan population data set, and evaluates the current status of Shanghai's 15min-CLC through the fusion of kernel density estimation, service area analysis and other statistical models and proposes relevant optimization suggestions. The results show that there are the following shortcomings: (1) From the perspective of different types of infrastructure service facilities, the spatial construction of Shanghai's overall life service facilities and shopping service facilities needs to be optimized. (2) From the perspective of comprehensive evaluation, the comprehensive service convenience of infrastructure service facilities in the downtown area is relatively high, while the comprehensive service convenience of urban infrastructure service facilities in the suburbs and outer suburbs is relatively low; The diversity of basic service facilities in the 15min-CLC in the downtown area is more consistent with the population distribution; However, in the peripheral areas of the urban area, too many infrastructure service facilities have been constructed. Based on the above shortcomings and the perspective of supply and demand matching, relevant optimization strategies are proposed in different regions and different types of infrastructure service facilities: (1) focus on the construction of basic service facilities in the urban fringe and urban-rural areas, improve the full coverage of the basic service facilities, and appropriately reduce the number of basic service facilities in the downtown area. (2) The development of community business models can be used to promote the development of new life service facilities and shopping service facilities. (3) Improve community medical institutions through facility function conversion, merger and reconstruction, etc. (4) Optimize the hierarchical basic service facility system and improve the population supporting facilities of basic service facilities in the 15min-CLC. This paper incorporates people's needs and concerns on the living environment into the 15min-CLC evaluation model, and uses Shanghai as an example to conduct research, summarizes the existing shortcomings, and proposes corresponding optimization strategies based on the matching of supply and demand. This article attempts to explore a replicable 15min-CLC planning model, so that it can be extended to the Yangtze River Delta urban agglomeration, to provide reference for further research on the 15min-CLC, and to promote urban construction under the concept of sustainable development.

Enhanced urban functional land use map with free and open-source data

ABSTRACT The study aims at developing an applicable methodology to produce the functional land-use map using only free and open-source data. Top-view Sentinel image and ground-view Open Street Map (OSM) data are chosen due to their extensive availability. The three-stage framework, including object-based image analysis, OSM data cleaning, and ontology-based decision fusion, is proposed and implemented with open-source tools. We applied the developed approach to districts 1, 4, and 7 of HoChiMinh city, representing the complexities of the dynamic change in big cities. The result showed a good functional land use map with 78.70% overall accuracy. The outcome presents the mismatch between the data-driven approach and human knowledge, which can be improved by ontology-based fusion with OSM data. The ontology-based framework comprises the common urban land-use classes and OSM attributes, which can be applied and extended in other urban areas. Additional text attributes may be applicable only locally and can be modified in our open-source framework. Object-based image analysis takes advantage of Google Earth Engine computing power, whereas ontology-based processing works well on a local computer. In future studies, adopted natural language processing to pre-process OSM data and ontology-based fusion will be implemented on the cloud-computing platform to enhance computational efficiency.

An evaluation of the use of GIS and open data in secondary school education in South Africa, with reference to QGIS and OpenStreetMap (OSM)

South Africa is one of only a few countries that has Geographic Information Systems (GIS) in the secondary school curriculum. Of these few, SA is even more singular as its Geography syllabus includes GIS geoprocessing. Results from this study show that only a minority of teachers teach practical GIS classes irrespective of their Examination Board, access to hardware, how resourced their school is or whether they teach at a private or a government school. The key determinants to teaching practical GIS lessons are the teacher’s perceived GIS expertise and access to spatial data and time. Software, connectivity, and power supply are also identified as challenges. Teachers who participated in the study overwhelming agree that there are numerous benefits to using GIS in the classroom. They also expressed a keen willingness to attend GIS courses and learn more about QGIS. A sample group evaluated how OpenStreetMap (OSM) could be used to create local spatial data and how QGIS can be used to teach the GIS curriculum and used to map local data for individual research projects. There is an urgent need for more current research, both globally and locally, into how GIS can be used more in secondary school pedagogy.

INNOVATION IN TEACHING: THE POLIMAPPERS COLLABORATIVE AND HUMANITARIAN MAPPING COURSE AT POLITECNICO DI MILANO

Abstract. Collaborative projects imply a wide variety of skills, ranging from technical abilities to teamwork and problem-solving attitudes. Innovative teaching programmes focused on the use and promotion of open-source geospatial tools represent a key element in developing such transversal abilities. OpenStreetMap (OSM) brings into play open and collaborative dynamics that build a critical ecosystem where single contributions are part of collective intelligence. This initiative led to the foundation of structured contributors groups such as PoliMappers, the first European YouthMappers chapter based in Politecnico di Milano (Italy). Pursuing the objective to build a responsible student-led community of Free and Open Source Software users, in 2021 PoliMappers organised the course entitled “Collaborative and Humanitarian Mapping”. The programme is structured into 7 meetings and aims to introduce university students to the OSM environment with a focus on the potential of open data and related geospatial tools, especially in the humanitarian field. It has seen the participation of the World Food Programme, Open Knowledge Foundation, TECHO Colombia, IMM Design Lab, Map for Future, UN Mappers, Spazio Vita Niguarda Onlus, Ledha Milano, AUS Niguarda Onlus and Humanitarian OpenStreetMap Team. Enrolled attendants’ contributions have been assessed with tools for quality review and statistics. Also, Q&As and suggestions to the attendants have been collected to detect common issues. The paper presents the course, from the design to the assessment of the students’ activities, and the lessons learned in this experience in such a way to provide some guidance to those who want to organize similar initiatives.

An investigation of traffic density changes inside Wuhan during the COVID-19 epidemic with GF-2 time-series images

In order to mitigate the spread of COVID-19, Wuhan was the first city to implement strict lockdown policy in 2020. Even though numerous researches have discussed the travel restriction between cities and provinces, few studies focus on the effect of transportation control inside the city due to the lack of the measurement and available data in Wuhan. Since the public transports have been shut down in the beginning of city lockdown, the change of traffic density is a good indicator to reflect the intracity population flow. Therefore, in this paper, we collected time-series high-resolution remote sensing images with the resolution of 1 m acquired before, during and after Wuhan lockdown by GF-2 satellite. Vehicles on the road were extracted and counted for the statistics of traffic density to reflect the changes of human transmissions in the whole period of Wuhan lockdown. Open Street Map was used to obtain observation road surfaces, and a vehicle detection method combing morphology filter and deep learning was utilized to extract vehicles with the accuracy of 62.56%. According to the experimental results, the traffic density of Wuhan dropped with the percentage higher than 80%, and even higher than 90% on main roads during city lockdown; after lockdown lift, the traffic density recovered to the normal rate. Traffic density distributions also show the obvious reduction and increase throughout the whole study area. The significant reduction and recovery of traffic density indicates that the lockdown policy in Wuhan show effectiveness in controlling human transmission inside the city, and the city returned to normal after lockdown lift.

Payoff-based Dynamic Segment Replication and Graph Classification Method with Attribute Vectors Adapted to Urban VANET

Due to the number of constraints and the dynamic nature of vehicular ad hoc networks (VANET), effective video broadcasting always remains a difficult task. In this work, we proposed a quality of video visualization guarantee model based on a feedback loop and an efficient algorithm for segmenting and replicating video segments using the Payoff-based Dynamic Segment Replication Policy (P-DSR). In the urban VANET environment, P-DSR is defined by taking into account the position of the vehicles, the speed, the direction, the number of neighboring vehicles, and the reputation of each node to stabilize the urban VANET topology. However, the management of various load control parameters between the different components of the urban VANET network remains a problem to be studied. This work uses a multi-objective problem that takes the parameters of our algorithm based on the Graph Classification Method with Attribute Vectors (GCMAV) as input. This algorithm aims to provide an improved class lifetime, an improved video segment delivery rate, a reduced inter-class overload, and an optimization of a global criterion. A scalable algorithm is used to optimize the parameters of the GCMAV. The simulations were carried out using the NetSim simulator and Multi-Objective Evolutionary Algorithms framework to optimize parameters. Experiments were carried out with realistic maps of Open Street Maps and its results were compared with other algorithms such as Seamless and Authorized Multimedia Streaming and P-DSR. The survey suggests that the proposed methodology works well concerning the average lifetime of the inter-classes and the delivery rate of video segments.

Automatic Detection of Impervious Surfaces from Remotely Sensed Data Using Deep Learning

The large scale quantification of impervious surfaces provides valuable information for urban planning and socioeconomic development. Remote sensing and GIS techniques provide spatial and temporal information of land surfaces and are widely used for modeling impervious surfaces. Traditionally, these surfaces are predicted by computing statistical indices derived from different bands available in remotely sensed data, such as the Landsat and Sentinel series. More recently, researchers have explored classification and regression techniques to model impervious surfaces. However, these modeling efforts are limited due to lack of labeled data for training and evaluation. This in turn requires significant effort for manual labeling of data and visual interpretation of results. In this paper, we train deep learning neural networks using TensorFlow to predict impervious surfaces from Landsat 8 images. We used OpenStreetMap (OSM), a crowd-sourced map of the world with manually interpreted impervious surfaces such as roads and buildings, to programmatically generate large amounts of training and evaluation data, thus overcoming the need for manual labeling. We conducted extensive experimentation to compare the performance of different deep learning neural network architectures, optimization methods, and the set of features used to train the networks. The four model configurations labeled U-Net_SGD_Bands, U-Net_Adam_Bands, U-Net_Adam_Bands+SI, and VGG-19_Adam_Bands+SI resulted in a root mean squared error (RMSE) of 0.1582, 0.1358, 0.1375, and 0.1582 and an accuracy of 90.87%, 92.28%, 92.46%, and 90.11%, respectively, on the test set. The U-Net_Adam_Bands+SI Model, similar to the others mentioned above, is a deep learning neural network that combines Landsat 8 bands with statistical indices. This model performs the best among all four on statistical accuracy and produces qualitatively sharper and brighter predictions of impervious surfaces as compared to the other models.

Analysing the Impact of Large Data Imports in OpenStreetMap

OpenStreetMap (OSM) is a global mapping project which generates free geographical information through a community of volunteers. OSM is used in a variety of applications and for research purposes. However, it is also possible to import external data sets to OpenStreetMap. The opinions about these data imports are divergent among researchers and contributors, and the subject is constantly discussed. The question of whether importing data, especially large quantities, is adding value to OSM or compromising the progress of the project needs to be investigated more deeply. For this study, OSM’s historical data were used to compute metrics about the developments of the contributors and OSM data during large data imports which were for the Netherlands and India. Additionally, one time period per study area during which there was no large data import was investigated to compare results. For making statements about the impacts of large data imports in OSM, the metrics were analysed using different techniques (cross-correlation and changepoint detection). It was found that the contributor activity increased during large data imports. Additionally, contributors who were already active before a large import were more likely to contribute to OSM after said import than contributors who made their first contributions during the large data import. The results show the difficulty of interpreting a heterogeneous data source, such as OSM, and the complexity of the project. Limitations and challenges which were encountered are explained, and future directions for continuing in this field of research are given.

Poverty Mapping in the Dian-Gui-Qian Contiguous Extremely Poor Area of Southwest China Based on Multi-Source Geospatial Data

Accurate information on the spatial distribution of poverty is of great significance to the formulation and implementation of the government’s targeted poverty alleviation policy. Traditional poverty mapping is mainly based on household survey data and statistical data, which cannot describe the spatial distribution of poverty well. This paper presents a study of mapping the integrated poverty index (IPI) in the Dian-Gui-Qian contiguous extremely poor area of southwest China. Based on multiple independent spatial variables extracted from NPP/VIIRS nighttime light (NTL) remote sensing data, digital elevation model (DEM), land cover information, open street map, and city accessibility data, eight algorithms were employed and compared to determine the optimal model for IPI estimation. Among these machine learning algorithms, traditional multiple linear regression had the lowest accuracy compared with the other seven machine learning algorithms and XGBoost showed the best performance. Feature selection was performed to reduce overfitting and five variables were finally selected. The final developed XGBoost model achieved an MAE of 0.0454 and an R2 of 0.68. The IPI map derived from the developed XGBoost model characterized the spatial pattern of poverty in the Dian-Gui-Qian contiguous extremely poor area well, which provided a good reference for the poverty alleviation work and public resources allocation in the study area. This study can also serve as a template for poverty mapping in other areas using remote sensing data.

Automated detecting and placing road objects from street-level images

Navigation services utilized by autonomous vehicles or ordinary users require the availability of detailed information about road-related objects and their geolocations, especially at road intersections. However, these road intersections are generally represented as point elements without detailed information, or are even not available in current versions of crowdsourced mapping databases including OpenStreetMap (OSM). This study proposes an approach to automatically detect road objects from street-level images and place them to correct locations according to urban rules. Our processing pipeline relies on two convolutional neural networks: the first one segments the images, while the second one detects and classifies the specific objects. Moreover, to locate the detected objects, we propose an attributed topological binary tree (ATBT) based on urban rules for each image in an image sequence to depict the coherent relations of topologies, attributes and semantics of the road objects. Then the ATBT is further matched with map features on OSM to determine the right placed location. The proposed method has been applied to a case study in Berlin, Germany. We validate the effectiveness of the proposed method on two object classes: traffic signs and traffic lights. Experimental results demonstrate that the proposed app roach provides promising results in terms of completeness and positional accuracy.

Mapping Community Engagement with Urban Crowd-Sourcing

Cities are highly dynamic entities, with urban elements such as businesses, cultural and social Points-of- Interests (POIs), housing, transportation and the like, continuously changing. In order to maintain accurate spatial information in these settings, crowd-sourcing models of data collection, such as in OpenStreetMap (OSM), have come under investigation. Like many crowd-sourcing platforms (e.g., Wikipedia), these geo- wikis exhibit tailing-off activity, bringing into ques- tion their long-term viability. In this paper, we begin an investigation into the sustainability of urban crowd-sourcing, by studying the network structure and ge- ographical mapping of implicit communities of con- tributors in OSM. We observe that spatially clustered crowd-sourcing communities produce higher coverage than those with looser geographic affinity. We discuss the positive implications that this has on the future of urban crowd-sourcing.

Task Specialization in Social Production Communities: The Case of Geographic Volunteer Work

In social production communities, users' individual and collective efforts lead to the creation of valuable resources — cf. Wikipedia, Open Street Map, and Reddit. Contributors to such communities often specialize in the tasks they choose to do. We found evidence for specialization by work type in Cyclopath, a geographic wiki for bicyclists -- most users edit a single type of map feature, such as points of interest or roads and trails. We also saw a user lifecycle effect: as users gain experience, they specialize in editing roads and trails. Our findings suggest more effective ways to organize social production interfaces, compose units of work, and match them to users who want to help.

Urban scene understanding based on semantic and socioeconomic features: From high-resolution remote sensing imagery to multi-source geographic datasets

Scene classification is a means to interpret high-resolution remote sensing (HRS) imagery, to obtain the high-level semantic information, which can provide a reliable reference for urban planning and monitoring. The traditional scene classification methods based on HRS imagery take uniform grid cells as the scene units, thereby missing the geographic boundaries and leading to the mosaicking effect. Therefore, in this paper, the urban scene is defined as a geographic unit bordered by the road network. The task of urban scene understanding is to recognize the socioeconomic or natural semantics of the urban scene. However, due to the complexity of the urban environment, the effectiveness of the traditional scene classification methods is limited on account of three problems when applied in urban scenes: 1) The lack of socioeconomic information in HRS images, with which it is difficult to discriminate diverse urban scenes with similar exteriors. 2) The large discrepancy in the sizes and shapes of urban land parcels affects the scene feature extraction and representation. 3) Urban scene understanding frameworks that can embed various scene classification models have rarely been studied. In this paper, to solve these problems, a universal urban scene understanding framework based on multi-source geographic data (USUMG) is proposed. In the USUMG framework, road network and water channel data from OpenStreetMap (OSM) are used for generating the urban scene units. For each irregular unit, a scene decomposition method based on a morphological skeleton is employed to represent the urban scene unit by unified processing patches. To integrate the different data sources, the high-level semantic features extracted from the HRS imagery and the socioeconomic features extracted from point of interest (POI) data are fused to determine the urban scene category. Finally, the USUMG framework with various scene classification methods was tested in urban districts of Wuhan and Macao in China to verify the universality and feasibility of the proposed framework. The experimental performances are provided in this paper as a benchmark for urban scene understanding based on multi-source geographic data.

An intelligent model to assist people with disabilities in smart cities

Smart cities emergence has allowed a wide variety of technological services to metropolitan areas. These services can improve life quality, minimize environmental impacts, improve health service, improve security, and bear the increasing number of people in the cities. Life quality encompasses many subjects, and accessibility for People with Disabilities (PwD) is one. In this article, smart cities focused on helping PwD are called Assistive Smart Cities (ASCs). In this sense, the article proposes a Model for Assistive Smart Cities called MASC. Related works do not cover geographically broad areas, such as cities and metropolitan regions. Moreover, they are not generic in terms of disabilities and are usually intended only for one type of disability. Given this scenario, the MASC covers large regions and supports various disabilities, such as hearing, visual impairment, and limitation of lower limb movements. Unlike the related works, MASC uses the interactions of PwD to compose histories of contexts offered as services. MASC proposes an ontology-based on ubiquitous accessibility concepts. The model evaluation focused on performance, functionality, and usability. Performance and functionality evaluations were performed using data generated by a context simulator called Siafu and data from the Open Street Maps (OSM) platform. Usability was evaluated using a smart wheelchair prototype. The results of usability show 96% acceptance regarding ease of use and 98% regarding system utility. The results indicate that the model supports massive applications, managing information to generate trails. Besides, MASC provides services for different types of users, namely PwD, healthcare professionals, and public administration.

Graph representation learning for road type classification

We present a novel learning-based approach to graph representations of road networks employing state-of-the-art graph convolutional neural networks. Our approach is applied to realistic road networks of 17 cities from Open Street Map. While edge features are crucial to generate descriptive graph representations of road networks, graph convolutional networks usually rely on node features only. We show that the highly representative edge features can still be integrated into such networks by applying a line graph transformation. We also propose a method for neighborhood sampling based on a topological neighborhood composed of both local and global neighbors. We compare the performance of learning representations using different types of neighborhood aggregation functions in transductive and inductive tasks and in supervised and unsupervised learning. Furthermore, we propose a novel aggregation approach, Graph Attention Isomorphism Network, GAIN1. Our results show that GAIN outperforms state-of-the-art methods on the road type classification problem.

Neighbourhood change and spatial inequalities in Cape Town

AbstractCapturing the dynamics of population change in urban areas necessitates access to geographically fine‐grained and temporally consistent data for several time points. Such data are generally not available and they must be created using standard population data which cannot usually be compared across time periods. In this paper, the focus is on changing spatial inequalities in Cape Town, South Africa. This paper details an approach to generation of gridded population counts (250 m by 250 m) for two census years – 2001 and 2011. Census data for Small Area Layers (SALs), Spot Building Count (SBC) data, and Open Street Map (OSM) landuse data were used to construct a grid of populated cells to which population counts are then reallocated. The reallocation of population counts from SALs to grid cells was undertaken using area‐to‐point kriging – an approach which is informed by the spatial variation in the population groups of interest as measured using the variogram. A case study based on population grids of unemployment rates shows how the grids can be used to chart changes and also to measure spatial inequalities across the city at two time points. The advantages of grids for capturing fine‐scale complexities and correctly accounting for physical separation between communities are demonstrated and the results show, while the broad patterns of inequality are consistent across time, there are pronounced increases in inequalities in some neighbourhoods. These areas – and what leads some areas to fall further behind – should be the subject of attention by policy‐makers.

Spatial parameters for transportation: A multi-modal approach for modelling the urban spatial structure using deep learning and remote sensing

A significant increase in global urban population affects the efficiency of urban transportation systems. Remarkable urban growth rates are observed in developing or newly industrialized countries where researchers, planners, and authorities face scarcity of relevant official data or geo-data. In this study, we explore remote sensing and open geo-data as alternative sources to generate missing data for transportation models in urban planning and research. We propose a multi-modal approach capable of assessing three essential parameters of the urban spatial structure: buildings, land use, and intra-urban population distribution. Therefore, we first create a very high-resolution (VHR) 3D city model for estimating the building floors. Second, we add detailed land-use information retrieved from OpenStreetMap (OSM). Third, we test and evaluate five experiments to estimate population at a single building level. In our experimental set-up for the mega-city of Santiago de Chile, we find that the multi-modal approach allows generating missing data for transportation independently from official data for any area across the globe. Beyond that, we find the high-level 3D city model is the most accurate for determining population on small scales, and thus evaluate that the integration of land use is an inevitable step to obtain fine-scale intra-urban population distribution.

Is Heathrow airport really a localised hot spot?

Location of observing sites mentioned in the text. The extent of London's built-up area is also shown. Base map obtained from OpenStreetMap (www.openstreetmap.org) and Open Street Map Foundation, licensed as CC BY-SA. (© OpenStreetMap contributors.) Daily maximum temperatures observed at London's Heathrow airport are compared with those of other official observing sites around the west and southwest of London to explore how representative Heathrow's temperatures are of the local environment. The frequency with which Heathrow is the warmest place in the UK is found to peak sharply between June and September. However, daily maximum temperatures at all of the London sites are usually similar, particularly at other times of the year.

ABNORMAL CROWDSOURCED DATA DETECTION USING REMOTE SENSING IMAGE FEATURES

Abstract. Quality is the key issue for judging the usability of crowdsourcing geographic data. While due to the un-professional of volunteers and the phenomenon of malicious labeling, there are many abnormal or poor quality objects in crowdsourced data. Based on this observation, an abnormal crowdsourced data detection method is proposed in this paper based on image features. This approach includes three main steps. 1) the crowdsourced vector data is used to segment the corresponding remote sensing imagery to get image objects with a priori information (e.g., shape and category) from vector data and spectral information from the images. Then, the sampling method is designed considering the spatial distribution and topographic properties of the objects, and the initial samples are obtained, although some samples are abnormal object or poor quality. 2) A feature contribution index (FCI) is defined based on information gain to select the optimal features, a feature space outlier index (FSOI) is presented to automatically identify outlier samples and changed objects. The initial samples are refined by an iteration procedure. After the iteration, the optimal features can be determined, and the refined samples with categories can be obtained; the imagery feature space is established using the optimal features for each category. 3) The abnormal objects are identified with the refined samples by calculating the FSOI values of image objects. In order to valid the effectiveness, an abnormal crowdsourced data detection prototype is developed using Visual Studio 2013 and C# programming, the above algorithms and methods are implemented and verified using water and vegetation categories as example, the OSM (OpenStreetMap) and corresponding imagery data of Changsha city as experiment data. The angular second moment (ASM), contrast, inverse difference moment (IDM), mean, variance, difference entropy, and normalized difference green index (NDGI) of vegetation, and the IDM, difference entropy and correlation and maximum band value of water are used to detect abnormal data after the selection of image optimal feature. Experimental results show that abnormal water and vegetation data in OSM can be effectively detected in this method, and the missed detection rate of the vegetation and water are all near to zero, and the positive detection rate reach 90.4% and 83.8%, respectively.

URBAN GEOMETRY: CITY SHAPE AND SPATIAL LAYOUT OF 6 INDONESIAN GOVERNMENT CENTERS

Indonesia is one of the fastest-growing countries. It has about 50% of the population in the ASEAN region. The country is currently evolving and changing. The recent highlight is the country's plan to create a new capital city – moving from the megalopolitan Jakarta in the Java island to a new remote area in East Kalimantan. However, research on Indonesian cities remains very limited to provide an evidence base for planning a new capital city. A fundamental question arises: Should we plan Indonesia's new capital city following western models, or should we plan the city using traditional Indonesian city patterns? This paper examines urban geometry to show similarities and differences in the government centers in 6 Indonesian provincial capitals. They are Jakarta, Bandung, Medan, Palembang, Surabaya, and the Special Region of Yogyakarta. The six capital cities will be analyzed and categorized using street grid patterns, street centrality, built-up area and open space distribution, and government center distances. This systematic and quantitative comparison of Cities will define the underlying traits and footprint in these Indonesian cities, and the findings could be considered suggestions and guidelines for the new Indonesian capital city. This study utilizes methods and measures using automation techniques to analyze and classify Indonesian government centers based on street networks and urban geometry. The methods include land use recognition techniques using satellite images; OpenStreetMap (OSM) processed using OSMnx; GIS multi-platform applications to run the spatial analysis. On the one hand, the study should contribute to urban design and capital city planning in Indonesia. On the other hand, it should provide methods for comparative studies of cities in developing countries.