scholarly journals Local Population Mapping Using a Random Forest Model Based on Remote and Social Sensing Data: A Case Study in Zhengzhou, China

2020 ◽  
Vol 12 (10) ◽  
pp. 1618
Author(s):  
Ge Qiu ◽  
Yuhai Bao ◽  
Xuchao Yang ◽  
Chen Wang ◽  
Tingting Ye ◽  
...  

High-resolution gridded population data are important for understanding and responding to many socioeconomic and environmental problems. Local estimates of the population allow officials and researchers to make a better local planning (e.g., optimizing public services and facilities). This study used a random forest algorithm, on the basis of remote sensing (i.e., satellite imagery) and social sensing data (i.e., point-of-interest and building footprint), to disaggregate census population data for the five municipal districts of Zhengzhou city, China, onto 100 × 100 m grid cells. We used a statistical tool to detect areas with an abnormal population density; e.g., areas containing many empty houses or houses rented by more people than allowed, and conducted field work to validate our findings. Results showed that some categories of points-of-interest, such as residential communities, parking lots, banks, and government buildings were the most important contributing elements in modeling the spatial distribution of the residential population in Zhengzhou City. The exclusion of areas with an abnormal population density from model training and dasymetric mapping increased the accuracy of population estimates in other areas with a more common population density. We compared our product with three widely used gridded population products: Worldpop, the Gridded Population of the World, and the 1-km Grid Population Dataset of China. The relative accuracy of our modeling approach was higher than that of those three products in the five municipal districts of Zhengzhou. This study demonstrated potential for the combination of remote and social sensing data to more accurately estimate the population density in urban areas, with minimum disturbance from the abnormal population density.

Author(s):  
X. F. Sun ◽  
X. G. Lin

As an intermediate step between raw remote sensing data and digital urban maps, remote sensing data classification has been a challenging and long-standing research problem in the community of remote sensing. In this work, an effective classification method is proposed for classifying high-resolution remote sensing data over urban areas. Starting from high resolution multi-spectral images and 3D geometry data, our method proceeds in three main stages: feature extraction, classification, and classified result refinement. First, we extract color, vegetation index and texture features from the multi-spectral image and compute the height, elevation texture and differential morphological profile (DMP) features from the 3D geometry data. Then in the classification stage, multiple random forest (RF) classifiers are trained separately, then combined to form a RF ensemble to estimate each sample’s category probabilities. Finally the probabilities along with the feature importance indicator outputted by RF ensemble are used to construct a fully connected conditional random field (FCCRF) graph model, by which the classification results are refined through mean-field based statistical inference. Experiments on the ISPRS Semantic Labeling Contest dataset show that our proposed 3-stage method achieves 86.9% overall accuracy on the test data.


2021 ◽  
Vol 10 (10) ◽  
pp. 681
Author(s):  
Xu Yin ◽  
Peng Li ◽  
Zhiming Feng ◽  
Yanzhao Yang ◽  
Zhen You ◽  
...  

The release of global gridded population datasets, including the Gridded Population of the World (GPW), Global Human Settlement Population Grid (GHS-POP), WorldPop, and LandScan, have greatly facilitated cross-comparison for ongoing research related to anthropogenic impacts. However, little attention is paid to the consistency and discrepancy of these gridded products in the regions with rapid changes in local population, e.g., Mainland Southeast Asia (MSEA), where the countries have experienced fast population growth since the 1950s. This awkward situation is unsurprisingly aggravated because of national scarce demographics and incomplete census counts, which further limits their appropriate usage. Thus, comparative analyses of them become the priority of their better application. Here, the consistency and discrepancy of the four common global gridded population datasets were cross-compared by combing the 2015 provincial population statistics (census and yearbooks) via error-comparison based statistical methods. The results showed that: (1) the LandScan performs the best both in spatial accuracy and estimated errors, then followed by the WorldPop, GHS-POP, and GPW in MSEA. (2) Provincial differences in estimated errors indicated that the LandScan better reveals the spatial pattern of population density in Thailand and Vietnam, while the WorldPop performs slightly better in Myanmar and Laos, and both fit well in Cambodia. (3) Substantial errors among the four gridded datasets normally occur in the provincial units with larger population density (over 610 persons/km2) and a rapid population growth rate (greater than 1.54%), respectively. The new findings in MSEA indicated that future usage of these datasets should pay attention to the estimated population in the areas characterized by high population density and rapid population growth.


Author(s):  
Fabiana Calçada de Lamare Leite ◽  
Aline Patrícia Henz

As recentes transformações econômicas, sociais e culturais produzem modificações no planejamento das áreas urbanas, principalmente a disposição de espaços de lazer, também caracterizados como atrativos turísticos. Sendo a cidade um ambiente de encontro, trocas e realizações, associando sua diversificação e possibilidades de apropriação ao dinamismo, a urbanidade torna-se um atributo importante para o estabelecimento e manutenção do convívio social. Na lógica da atividade turística, a cidade é entendida como um produto que deve ter seus atributos desenvolvidos e direcionados à satisfação dos turistas. Para despertar o interesse do visitante, o planejamento local deve utilizar parâmetros de configuração dos instrumentos e equipamentos da cidade. A temática da atratividade associada à visitação, esta estritamente relacionada ao consumo do espaço. Consumo, que pode ser entendido como alvo de conhecimento, interesse em vivenciar determinado espaço com suas particularidades que o identificam, despertando a motivação em usufruir de suas singularidades. O objetivo central deste artigo é qualificar os principais parques urbanos de Curitiba apresentados como atrativos turísticos, no entendimento de que esta disposição espacial somada a oferta de serviços e infraestrutura potencializam sua atratividade para a atividade turística. De acordo com a Prefeitura Municipal, Curitiba dispõe de 17 parques urbanos (PMC, 2007), no entanto, esse trabalho limita-se a apresentar oito parques que estão inseridos no roteiro realizado pela Linha Turismo. A escolha desse universo justifica-se pelo fato de que essas localidades são atendidas pela Linha Turismo, um serviço turístico já consolidado na cidade que atende a diversos pontos, agregando atratividade e valor turístico por sua funcionalidade. A metodologia é de abordagem qualitativa e como técnicas, a pesquisa utilizou-se de pesquisa bibliográfica e observação direta. Foi possível demonstrar que a lógica de organização da cidade, esta cada vez mais relacionada a lógica da atividade turística e que, é interesse de ambas a integração e a socialização de interesses. O planejamento da cidade ocorrendo de maneira articulada ao planejamento do turismo é a condição para a ocorrência de um turismo atrativo e competitivo para a localidade. Além disso, as duas práticas ocorrendo de maneira articulada podem beneficiar o desenvolvimento local, influenciando na qualidade de vida da população local e, consequentemente, beneficiando a atividade turística. Urban Parks at Curitiba (PR, Brazil): Spatiality, Planning and Tourism ABSTRACT Recent economical, social and cultural transformations are causing changes on the planning of urban areas, mainly those relative to the disposition of leisure spaces, also characterized as touristic attractions. As the city is an environment of encounter, exchange and fulfillments, with its diversity and appropriation possibilities being associated to dynamism, urbanism becomes an important asset for the establishment and maintenance of social cohabitation. Under the logics of touristic activity, cities are comprehended as a product that requires its attributes to be developed and directed towards the satisfaction of tourists. So as to stimulate the visitor’s interest, local planning must apply configuration parameters of the city´s instruments and equipment. The issue about attractiveness in relation to visits, is strictly linked to space consumption. Consumption can be understood as the aim for knowledge, the interest to enjoy certain space with the characteristics that make it particular, motivating towards the use of its singularities. The main objective of this article is to describe the main urban parks of Curitiba presented as tourist attractions, which are presented as touristic attractions, as it is considered that such a spatial disposition, added to a service and infrastructure offer, augment their attractiveness for touristic activities. According to the Municipal Mayor´s office, Curitiba has seventeen (17) urban parks (PMC, 2016), however this research is limited to eight parks that are included in the itinerary deigned by the Linha Turismo. The selection of this universe is justified by the fact that they are places attended by the Linha Turismo, a consolidated touristic service in the city, which attends different spots, adding attractiveness and touristic value due to its functionality. It was possible to demonstrate that the city´s configuration logic is increasingly related to touristic activity, and that both issues appreciate the integration and socialization of interests. City planning, when articulated to tourism planning, is the basic condition to accomplish a touristic attractiveness and competitiveness for such a place. Furthermore, when both practices are articulated in their evolution, they benefit local development, thus having an impact on the wealth of the local population, and also benefitting touristic activity. KEYWORDS: Tourism; Planning; Urban Parks; Curitiba (PR, Brazil).


2020 ◽  
Vol 9 (8) ◽  
pp. 466
Author(s):  
Yue Deng ◽  
Jiping Liu ◽  
An Luo ◽  
Yong Wang ◽  
Shenghua Xu ◽  
...  

Understanding the balance between the supply and demand of leisure services (LSs) in urban areas can benefit urban spatial planning and improve the quality of life of residents. In cities in developing countries, the pursuit of rapid economic growth has ignored residents’ demand for LSs, thereby leading to a high demand for and short supply of these services. However, due to the lack of relevant research data, few studies have focused on the spatial mismatch in the supply and demand of LSs in urban areas. As typical representatives of multisource geographic data, social sensing data are readily available at various temporal and spatial scales, thus making social sensing data ideal for quantitative urban research. The objectives of this study are to use openly accessible datasets to explore the spatial pattern of the supply and demand of LSs in urban areas and then to depict the relationship between the supply and demand by using correlation analysis. Therefore, taking Beijing, China, as an example, the LS supply index (SI) and societal needs index (SNI) are proposed based on open data to reflect the supply and demand of LSs. The results show that the spatial distribution of the LS supply and demand in Beijing varies with a concentric pattern from the urban center to suburban areas. There is a strong correlation between the supply and demand of commercial and multifunctional services in Chaoyang, Fengtai, Haidian and Shijingshan, but there is no obvious correlation between the supply and demand of ecological and cultural services in Beijing. Especially in Dongcheng and Xicheng, there is no obvious correlation between the supply and demand of all services. The proposed approach provides an effective urban LS supply and demand evaluation method. In addition, the research results can provide a reference for the construction of “happy cities” in China.


2020 ◽  
Vol 12 (6) ◽  
pp. 1032
Author(s):  
Shengyu Xu ◽  
Linbo Qing ◽  
Longmei Han ◽  
Mei Liu ◽  
Yonghong Peng ◽  
...  

For urban planning and environmental monitoring, it is essential to understand the diversity and complexity of cities to identify urban functional regions accurately and widely. However, the existing methods developed in the literature for identifying urban functional regions have mainly been focused on single remote sensing image data or social sensing data. The multi-dimensional information which was attained from various data source and could reflect the attribute or function about the urban functional regions that could be lost in some extent. To sense urban functional regions comprehensively and accurately, we developed a multi-mode framework through the integration of spatial geographic characteristics of remote sensing images and the functional distribution characteristics of social sensing data of Point-of-Interest (POI). In this proposed framework, a deep multi-scale neural network was developed first for the functional recognition of remote sensing images in urban areas, which explored the geographic feature information implicated in remote sensing. Second, the POI function distribution was analyzed in different functional areas of the city, then the potential relationship between POI data categories and urban region functions was explored based on the distance metric. A new RPF module is further deployed to fuse the two characteristics in different dimensions and improve the identification performance of urban region functions. The experimental results demonstrated that the proposed method can efficiently achieve the accuracy of 82.14% in the recognition of functional regions. It showed the great usability of the proposed framework in the identification of urban functional regions and the potential to be applied in a wide range of areas.


2019 ◽  
Vol 11 (5) ◽  
pp. 574 ◽  
Author(s):  
Xuchao Yang ◽  
Tingting Ye ◽  
Naizhuo Zhao ◽  
Qian Chen ◽  
Wenze Yue ◽  
...  

Fine-resolution population distribution mapping is necessary for many purposes, which cannot be met by aggregated census data due to privacy. Many approaches utilize ancillary data that are related to population density, such as nighttime light imagery and land use, to redistribute the population from census to finer-scale units. However, most of the ancillary data used in the previous studies of population modeling are environmental data, which can only provide a limited capacity to aid population redistribution. Social sensing data with geographic information, such as point-of-interest (POI), are emerging as a new type of ancillary data for urban studies. This study, as a nascent attempt, combined POI and multisensor remote sensing data into new ancillary data to aid population redistribution from census to grid cells at a resolution of 250 m in Zhejiang, China. The accuracy of the results was assessed by comparing them with WorldPop. Results showed that our approach redistributed the population with fewer errors than WorldPop, especially at the extremes of population density. The approach developed in this study—incorporating POI with multisensor remotely sensed data in redistributing the population onto finer-scale spatial units—possessed considerable potential in the era of big data, where a substantial volume of social sensing data is increasingly being collected and becoming available.


Author(s):  
Shen ◽  
Zhou ◽  
Li ◽  
Zeng

Fine spatiotemporal mapping of PM2.5 concentration in urban areas is of great significance in epidemiologic research. However, both the diversity and the complex nonlinear relationships of PM2.5 influencing factors pose challenges for accurate mapping. To address these issues, we innovatively combined social sensing data with remote sensing data and other auxiliary variables, which can bring both natural and social factors into the modeling; meanwhile, we used a deep learning method to learn the nonlinear relationships. The geospatial analysis methods were applied to realize effective feature extraction of the social sensing data and a grid matching process was carried out to integrate the spatiotemporal multi-source heterogeneous data. Based on this research strategy, we finally generated hourly PM2.5 concentration data at a spatial resolution of 0.01°. This method was successfully applied to the central urban area of Wuhan in China, which the optimal result of the 10-fold cross-validation R2 was 0.832. Our work indicated that the real-time check-in and traffic index variables can improve both quantitative and mapping results. The mapping results could be potentially applied for urban environmental monitoring, pollution exposure assessment, and health risk research.


2021 ◽  
Vol 67 (3) ◽  
pp. 425-439
Author(s):  
Faiz Ahmed Chundeli ◽  
Kusum Lata ◽  
Adinarayanane Ramamurthy ◽  
Minakshi Jain

In this article, a critical assessment of urban density and Covid-19 incidences in Indian cities is explored. The top hundred Covid-19 reported districts are analysed. The ArcGIS 10.1 statistical tool Getis-Ord Gi* is used in the identification of statistically significant Covid-19 clusters across India. Attempts are made to empirically establish the correlation between the urban density, the number of reported cases, and their possible impact on health infrastructure in general and planning in specific. Based on the results from 164 out of 693 district datasets, analyses have shown high positive spatial autocorrelation, which is more than 24% of the districts analysed. Further, the results show that southern districts are more affected than the Central and northern districts of India. Although a positive association between reported cases and the urban density was found, in high-density urban areas, the relationship with infection rate varied, which should be looked at together with other variables such as people’s activities and behaviours.


Author(s):  
Ha Thi Thu Pham ◽  
◽  
Toan Kim Tran

Throughout time and history, urbanization has proven itself to be a significant impact on climate in urban areas. In this study, we investigate urbanization effect on temperature trends in several regions across Vietnam based on statistical relationship between these trends and local population growth as well as the change in the annual mean temperature in the past decade by applying statistical analysis to the results. Population data from 2008-2018 and the temperature data from 1988-2018 were obtained from the Annual Abstracts of Statistics and the Institute of Hydrology and Meteorology Science and Climate Change, respectively. Although most of our findings indicate a very small correlation between temperature rise and local population growth, there were exceptions with reasonable values. The results suggest that urbanization contributes to the change in temperature trend of different regions. The type of region (based on its population) also determines if the change in temperature is positively or negatively correlated with the population growth. Furthermore, by using ArcMap, we also constructed several surface temperature maps in the past few decades in order to gain further insights into how temperature changed with time.


Sign in / Sign up

Export Citation Format

Share Document