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In order to solve many emerging social and economic problems brought by the rapid economic development as well as the population increase in the past decades, many ideas and solutions are proposed, in which one of them is urban compact development. However, due to the difficulty of obtaining data as well as the “unclearness in the definition of urban functional compactness,” few reliable methods have been developed to address the problem of urban compactness measurement. But thanks to the “increasing availability of public source data” as well as the advancement of technologies such as POIs (points of interest), GIS (geographic information systems) and VIIRS(Visible Infrared Imaging Radiometer Suite), human activities and data can be better analyzed and understood. With these as prerequisites, in this paper the authors have developed the Functional Compactness Index(FCI) which seeks to “(1) it can distinguish the differences in the urban functional compactness among cities; (2) its computation procedure should be objective enough to ensure the soundness of its results; and (3) it should be suitable for large-scale research in term of input data availability.”
In the study, the authors use the data of POIs, VIIRS, RNO (Roads Network of OpenStreetMap) from four Chinese cities of Beijing, Shanghai, Xi’an and Xiamen to explore and analyze the difference in their urban compactness. FCI works by first “[taking] the street blocks in RNO data as the basic spatial analysis unit. Then, the FCI identifies the functional attributes of each street block according to the POIs. Finally, it uses NPP/VIIRS nighttime light data to determine the intensity of human activity in functional zones.” POIs are used to determine the functionality of every basic street block units. They are “identified based on the proportion of each kind” out of six kinds and the belonging of a street block is determined by which functionality has the largest number of points. Meanwhile the intensity of human activity is based on the data from VIIRS. After analyzation, it concludes that “the degree of mixing between the residential zone and the other zones is greater, the average distance between the residential zone and other zones is shorter, and the urban function is more compact.” Moreover, higher human intensity means more compactness in the city. Generally, the intensity of human activities is highest near the city center, gradually decreases from center to the city limits, increases again at city sub-centers, then continues decreasing.
In conclusion, the authors have proved that greater human intensity means more compact city functions; also, the mixing of residential zones with other zones is positive correlated with the compactness of a city. The authors also provide solutions to address the problems in the city. For example, “By shortening the average distance between residential zones and other zones, urban activity can be increased, and urban functional compactness can be improved.” The FCI can both reflect the overall compactness of a city as well as local compactness; for example, smaller cities like Xiamen and Xi’an have a bigger and more balanced FCI (8.59, 6.63) while bigger cities like Shanghai and Beijing have a smaller FCI (5.78, 4.69) but “FCI is high in the center of the city and low in the periphery.” It shows that in the smaller cities, the intensity of human activity is more balanced, and the overall functional compactness is optimal.
This paper is related to Sen’s definition of human development by examining and analyzing the data from urban areas to develop a new methodology to address the social and economic problems arise in the context of Urbanization. The goal of sustainable cities and communities is in relation to this article. The datasets of POIs, RNO, GIS and map resources are used. The authors try to measure the urban compactness in a comprehensive and detailed way in order to solve problems in urbanization.
Reference:
Lan, T., Shao, G., Xu, Z., Tang, L., & Sun, L. (2021). Measuring urban compactness based on functional characterization and human activity intensity by integrating multiple geospatial data sources. Ecological Indicators, 121, N.PAG. https://doi.org/10.1016/j.ecolind.2020.107177
Fine-scale population distribution data are playing a more and more important role in today’s society as data science develops rapidly. However, most studies focus on the large-scale population distribution in country and city level while microscale population distribution at local level are often ignored. In this study, the authors introduce a framework that “[maps] the population distribution at the building level” with geospatial data. Then, they apply the model to analyze and map the population distributions at the building level of five central districts in Guangzhou, China. The purpose of the study is to better understand the increasing residential and migration population’s “environmental and ecological effects” on the city as well as the complex population behaviors.
In this study, multiple spatial data sets are used, including the POIs and APIs (application programming interfaces) provided by Baidu, and the most important Realtime Tencent User Density (RTUD) provided by Tencent. The reason being of using the private company Tencent’s data is that its users in China have reached 808 Million, while covering more than 93% of the population in China’s largest cities (Beijing, Shanghai, Guangzhou). Thus, Tencent’s user data can be seen as “a type of bias sampling of the general population dynamic distribution.” This is how the methodology works: (1) Choose the high-population POIs and map the preliminary population disaggregation. (2) Build a nonlinear population model by integrating the RFA and several sources of geospatial big data. (3) Calculate the microscale population distributions by using the proposed model and compared the reliability of the results with census data and results from other methods.
The results have shown that the top three categories for population intensity are clinical facilities, residential communities, and education. The researchers have found out that the nighttime RTUD correlates to population density the most, contributing 13.8%; life service and educational facility follows close behind, contributing 13.66% and 12.4%, respectively. The model also calculated the Densely Inhabited Index(DII) at the street level; the center of the city has the highest DII, indicating that the average housing area of individuals living at center districts are substantially smaller, possibly due to high housing prices as well as old buildings. Another interesting phenomenon is the “urban village,” which has an extremely high DII (3.68 for Tianhe Town and 7.00 for Xiaozhou Village) but are located in the outskirts of the city. It is possibly attributed to the “migration of labor-intensive enterprises from downtown and the poor residential buildings and infrastructure.”
By comparison, the proposed model of the authors has the highest accuracy of mapping population density at local/building level. The results result fit the official per capita housing area reasonably and accurately according to a comparison with official statistical housing data. This model is effective at mapping high-precisions microscale urban population distribution at a fine spatial resolution of 25 m, which will be very useful after further development to address social issues.
This paper is related to Sen’s definition of human development by introducing a new methodology that maps often-ignored microscale population distribution to better understand and study the migration pattern and human behaviors in the context of Urbanization. The goal of sustainable cities and communities is in relation to this article. The datasets of POIs, GIS, RTUD and APIs are used, provided by trustworthy private companies of Tencent and Baidu. The authors try to precisely measure the urban inhabitant patterns in a comprehensive way to solve problems in urbanization.
Reference: Yao, Y., Liu, X., Li, X., Zhang, J., Liang, Z., Mai, K., & Zhang, Y. (2017). Mapping fine-scale population distributions at the building level by integrating multisource geospatial big data. International Journal of Geographical Information Science, 31(6), 1220–1244. https://doi.org/10.1080/13658816.2017.1290252
The key to align the ever-growing urban population and sustainability is the Information and Communication Technologies (ICTs). The Smart City movement emerged from ICTs aiming to improve the quality of life of urban population as well as make the cities more sustainable. However, the core concept and definition of smart cities are too vague to be applied practically. In this paper, the authors define the concept of Smart Cities, introduce a new model that has sustainability in mind as well as explain the “geomatics pervasiveness” of Smart Cities using cases.
Nowadays, cities are playing a more and more important role in the world. Over half of world’s population live in a city; It has become an “important actor for innovation and development, and economic growth.” A declining living environment in cities will have a huge impact on the economics and prosperity is not an exaggeration. The author argues that Smart Cities is a key solution to urban development challenges such as “urban sprawl, environmental challenges and sustainability, transportation, high costs of management, civic participation, energy constraints, enhancement of cultural heritage, citizens’ quality of life, etc.” The three main components of Smart Cities are economic, social and environmental. The envisioned approaches to Smart Cities rely on technologies to deliver information and services to its citizens. Delivery needs a destination; therefore, location becomes the core information; GPS (Global Positioning System) technology and data will be applied widely in the Smart Cities. Then the authors discuss the examples involving geomatics that provides solutions to actual problems. The authors also examine the geomatics technologies and its applications, including GISs (Geographic Information Systems), GNSSs (Global Navigation Satellite Systems), MLS (mobile laser scanning), as well as MAR (Mobile Augmented Reality). Although the integration and application of ICTs and geomatics tools are crucial to the concept of Smart Cities, the authors argued that there needs to be “a vision shared by all the actors of the Smart City,” including economics, social and environmental components. Since the transformation from a normal city to a smart city is a long process, a careful and detailed planning is required; Colleges and universities would serve great advisors to policy makers due to their neutral position as well as expertise in the field.
With the forthcoming technological progresses in the field of geomatics and data science, we can better model the urban environment and optimize it with new techniques. However, Smart City goes way beyond the integration of technology and cities, it also aims to be more “civic (citizen-centered), attractive, prosperous and sustainable” with the combined efforts of scientists, policy makers, and citizens. The authors envision Smart Cities will be able to “rethink the city, to involve citizens, to ensure better governance, participatory and transparent, truly focused on the citizen, the economic development of the city and its sustainability.”
Reference: Doran, M.-A., & Daniel, S. (2014). Geomatics and Smart City: A transversal contribution to the Smart City development. Information Polity: The International Journal of Government & Democracy in the Information Age, 19(1/2), 57–72. https://doi.org/10.3233/IP-140330
The growth of world population in the past decades has brought both positive and negative effects to the society, especially to the urban setting; along with the rapid economic development, a booming population also leads to “overcrowding in cities and loss of valuable green areas and indigenous vegetation within and outside the city.” The authors highlighted several important techniques that help the researchers assess and monitor urban development, including Land Use and the Land Cover (LULC), remote sensing and GIS.
Population is center in all geospatial as well as urban studies. Here the authors introduce the target city of the study, Lahore in Pakistan, citing its population data of the past centuries that results in a sixfold increase due to factors such as rural to urban migration.
Like many other geospatial studies, the image-processing technique of remote sensing is crucial in this study, creating a base map that “is the only entity which directs the data to a clear spatial dimension;” along with surveying data from the Survey of Pakistan and other government institutions, the researchers have created a suitable base map for GIS modeling.
Since the researchers are interested in the past development of the city of Lahore, the source maps were scanned for digitalization. So that the growth map of Lahore can be developed; during the period of 63 years since Independence, Lahore has “has resulted in major development in south and southeast directions” due to physical barriers and national boundary; it is also worth noting that the physical growth of the city “has been along major roads.”
In conclusion, the authors argue that the development of cities are affected by “numerous constraints” such as rivers and borders; additionally, “intangible constraints” that are not easy to quantify also contributes to the physical configuration of a city, such as “congestion at the City center, traffic problems, solid waste management, and depleted housing conditions.” Despite the setbacks, the city of Lahore still grew rapidly in the past decades, which can be seen from the loss of vegetations. Based on the evidence, the authors suggests the future development direction of Lahore as well as vows for the protection of Lahore’s local vegetation. Systematic monitoring of the urban development, proper management of the City and planned future development would lead to improvement in the living standards and environmental conditions of the millions of people living in the city; Remote sensing along with the GIS technology can be used effectively and economically in the analysis and the inventory of urban development, land use / land cover study of the urban settlements and the vegetation.
This paper is related to Sen’s definition of human development by applying the geospatial technologies such as remote sensing and GIS to map and analyze an actual city, identifying its development trend and current issues. The goal of sustainable cities and communities is in relation to this article. The datasets of GIS, Survey of Pakistan are used; the technologies include remote sensing and geospatial scanning and mapping.
Reference: Shirazi, S. A., & Kazmi, S. J. H. (2014). Analysis of Population Growth and Urban Development in Lahore-Pakistan using Geospatial Techniques: Suggesting some future Options. South Asian Studies (1026-678X), 29(1), 269–280.
In conclusion, the four articles all discuss the social and economic problems that overloaded urban population has brought to the society. They provide solutions by either introducing a new concept, technique or analyzing the population trend of an existing city. I have found it effective and useful to apply geospatial technologies to obtain data about population movement and trend, namely, breaking down the large chuck of population into units of blocks and buildings, therefore identifying and mapping the local functionalities and the population compactness of a city. The data and population maps obtained will be analyzed to better understand urban population as well as to improve and optimize the urban environment.