Beijing Metropolitan Area Research Articles

Urban pluvial flood susceptibility mapping based on a novel explainable machine learning model with synchronous enhancement of fitting capability and explainability

Urban pluvial flood susceptibility mapping aims to identify flood-prone areas in cities. Machine learning methods are increasingly used, but typically face a trade-off between accurate identification of flood-susceptible areas and development of effective local mitigation strategies. To synchronously enhance fitting capability and explainability, this study introduced a novel machine learning model, Residual-Logistic Regression (RES-LR). RES-LR is developed by integrating Artificial Neural Network (ANN) and Logistic Regression (LR). The model adopts ANN to form its explainability module, which increases model complexity to ensuring the model’s fitting ability. Meanwhile, the explainability module was incorporated into the LR structure through residual connections to provide parametric expressions for local feature contributions, enabling the identification of key flood conditioning factor by site. The model was applied to the Beijing Metropolitan Area, achieving an accuracy rate exceeding 80% in identifying flooded sites. Based on its local explainability, the model unveiled the diverse flood formation mechanism in the study area and the complex impact of drainage-related factors. These results were informative for formulating site-specific flood mitigation measure. This study represents a significant advancement in the application of explainable machine learning for systematic flood management.

Urban waterlogging susceptibility assessment based on hybrid ensemble machine learning models: A case study in the metropolitan area in Beijing, China

Urban waterlogging has emerged as a significant problem worldwide, particularly in densely populated cities. Accurate assessment of waterlogging susceptibility at the city scale is crucial for mitigating the risks associated with waterlogging and optimizing municipal design accordingly. However, existing studies on urban waterlogging susceptibility assessment have primarily relied on individual machine learning models. It is worthwhile to explore whether hybrid ensemble models have the potential to enhance the predictive performance. This research presents two hybrid ensemble machine learning models, namely Stacking and Blending, for assessing urban waterlogging susceptibility in the metropolitan area of Beijing, China. The performances of these models are compared with those of the widely used individual machine learning models. Evaluation of all the models is based on metrics such as Accuracy rate and Area Under Curve (AUC) score. The results demonstrate that the Stacking and Blending models consistently outperform the traditional machine learning models, such as Random Forest, Logistic Regression, etc. Through susceptibility analysis and model interpretation with SHAP method, this paper obtains several key findings that low lying areas may not necessarily be areas with severe waterlogging; urban roads and densely populated areas are highly susceptible to becoming high-risk areas for waterlogging in the study area. This study not only highlights the effectiveness of the Stacking and Blending models for urban waterlogging susceptibility assessment but also provides valuable insights for waterlogging mitigation strategies in urban planning.

Spatial variations of plant species diversity in urban soil seed banks in Beijing, China: Implications for plant regeneration and succession

The near-to-nature urban forestry concept and practices are widely recognized for urban greening, urban ecosystem restoration, urban greenspace management for biodiversity conservation and ecosystem services provision. However, the regeneration and succession of urban vegetation are rarely studied due to the complex settings of the urban environment. To this end, we conducted a large-scale field investigation in the metropolitan area of Beijing, China to explore the spatial variations in plant species composition and diversity in soil seed banks, and their similarity to the aboveground vegetation to assess the potential of urban plant regeneration. Overall, 657 vegetation and soil sampling plots from 219 grids, measuring 2 km × 2 km each, were investigated within two perpendicular 10 km wide transects running across the urban center in north-south and east-west directions within the 6th Ring Road of the city. We recorded a total of 102 plant species in soil seed banks, including 13 tree species, 10 shrub species, and 79 herb species. We found that the soil seed bank species diversity and its similarity to that of the aboveground vegetation communities decreased significantly with the urbanization intensity. Higher urbanization intensity is typically associated with increased human management and a reduction in Greenspace Area (GSA). Soil seed bank species richness increased significantly when GSA exceeded 45 % and the similarity of species composition and diversity between soil seed banks and aboveground vegetation communities was the highest in forest parks. This suggests that habitats under forest park management are more conducive to plant regeneration. Soil seed bank species diversity first increased and then decreased significantly with increased distance to the city center, whereas the species similarity between the soil seed banks and the aboveground vegetation communities showed little change with the ring roads going out. The results of this study have important implications for further understanding the potential for urban vegetation regeneration and sustainability, which have significant implications for urban biodiversity conservation and restoration.

Characteristics of urban park recreation and health during early COVID-19 by on-site survey in Beijing

The positive health effects of green space have received increasing attention, however, on-site surveys and city-level research to reveal the relationship between urban park recreation and urbanite health in metropolitan areas during a post-pandemic period are lacking. We conducted an on-site survey using a questionnaire with 225 respondents from 22 urban parks distributed across the metropolitan area of Beijing during the early COVID-19 eased period with another 1346 respondents in 2021 to make verification. We identified factors that could influence public perceptions of park quality and human health (i.e., physical, mental, and social health) and revealed gender differences in perceptions of park characteristics. The correspondence pattern of perceived urban park quality with social health is distinct from that of physical and mental health. Due to the strict social distancing policy in early COVID-19 period, urban parks in different levels of urbanization environment could exert varied health effects.

THE SPATIAL-TEMPORAL DIFFERENTIATION IN AIR POLLUTION AND INDUSTRY STRUCTURE — EVIDENCE FROM THE METROPOLITAN AREA OF BEIJING–TIANJIN–HEBEI URBAN IN CHINA

In developing countries, environmental problems are often intertwined with industrial spatial distribution and migration. This paper is the first in this field to apply the standard deviational ellipse (SDE) method to the analysis of spatial agglomeration and spatiotemporal evolution of air pollution in 13 cities in the Beijing–Tianjin–Hebei region (BTH). According to the industrial classification, each industry could be further divided into five sub-industries: labor-intensive, capital-intensive, technology-intensive, pollution-intensive, and the mining industry. The discussion includes an exploration of the relationship between environmental quality and the spatial distribution of different sub-industries, along with their dynamic changes in migration. This paper also describes the innovative use of the geographically weighted regression (GWR) to study the impact of the whole industry and sub-industries on the environment. The main conclusions of this paper are as follows: (i) Both air pollution and the industry have obvious spatial agglomeration characteristics. (ii) The spatial difference coefficients between the secondary industry and environmental pollution and between various sub-industries and environmental pollution are small, and there is spatial consistency. (iii) Capital-intensive industries represent a primary source of air pollution. In addition to the slowdown of the tertiary industry, the contribution of various types of sub-industries to environmental pollution displays an increasing trend.

Housing Prices and the Characteristics of Nearby Green Space: Does Landscape Pattern Index Matter? Evidence from Metropolitan Area

This study aimed to examine the association between housing prices and green space characteristics with a special focus on exploring the effects of the shape pattern index. The research was based on a hedonic price model across two main distance buffers from residential properties to urban green spaces. Green spaces were characterized by size and shape measured by a landscape shape index (LSI). This study was based on 16,222 housing transaction data obtained from the website of real estate agencies during December 2019 in the Metropolitan Area of Beijing. Linear regression and semi-log regression analysis were used to examine the associations between independent housing and neighborhood characteristic variables and housing prices. The results suggested that a one-unit increase in the natural logarithm of the landscape shape index (LSI) can increase housing prices by 4% (5543 CNY ≈ 826 USD). Such marginal effects were more pronounced for residences located close to urban green spaces and tended to decay as the distance from residences to green spaces increased. Additional analysis captured the marginal effects of the natural logarithm of the landscape shape index (LSI > 1.3) on achieving the maximum monetary evaluation of the property. The findings of this study suggest that the effects of specific green space characteristics on housing prices should be taken into account in landscape and urban design.

Variations in Sap Flux Density of Three Urban Tree Species and Their Main Environmental Influencing Factors in Different Timescales in the Beijing Metropolitan Area

Despite the importance of landscape design and water-resources management for urban planning, urban-forest transpiration was seldom estimated in situ. Detailed data on different urban trees’ water resource use and the effect of climatic fluctuations on their transpiration behaviour in different timescales are limited. In this study, we used a thermal dissipation method to measure the sap flux density (Js) of three urban tree species (Pinus tabulaeformis Carrière, Cedrus deodara (Roxb.) G. Don, and Robinia pseudoacacia Linn.) from 1 May 2008 to 30 April 2016 in Beijing Teaching Botanical Garden. The effects of environmental factors on sap flux density (Js) in different timescales were also analyzed. The results showed that there were significant differences in the sap flux density of three trees species in daily, seasonal, and interannual timescales. The hourly, seasonal, and interannual mean sap flux density of Pinus tabulaeformis were higher than that of Cedrus deodara and Robinia pseudoacacia. The seasonal mean Js of Pinus tabulaeformis, Cedrus deodara, and Robinia pseudoacacia in summer were 18.67, 16.19, and 41.62 times that in winter over 2008–2015. The annual mean sap flux density of Pinus tabulaeformis was 1.25–1.72 and 1.26–1.82 times that Cedrus deodara and Robinia pseudoacacia over 2008–2015. The Js responses in three tree species to environmental factors varied differently from daily to interannual timescales. The pattern of day-to-day variation in Js of three urban tree species corresponded closely to air temperature (Ta), soil temperature (Ts), solar radiation (Rs), and vapor pressure deficit (VPD). The Jarvis–Stewart model based on Ta, Rs, and VPD was more suitable for the sap flux density simulation of Pinus tabulaeformis than Cedrus deodara and Robinia pseudoacacia. The main factor affecting the sap flux density of Pinus tabulaeformis and Cedrus deodara was Ta in seasonal timescales. However, the main factor affecting the sap flux density of Robinia pseudoacacia was Ts. The interannual variations in the Js of Pinus tabulaeformis and Robinia pseudoacacia were mainly influenced by wind speed (w) and soil water content (SWC), respectively. The selected environmental factors could not explain the variation in the sap flux density of Cedrus deodara in an interannual timescale. The findings of the present study could provide theoretical support for predicting the water consumption of plant transpiration under the background of climate change in the future.

Characteristics of High-Technology Industry Migration within Metropolitan Areas—A Case Study of Beijing Metropolitan Area

Industrial migration is a hot topic in economic geography. This study traces the migratory process of key high-tech enterprises in Beijing during 2008–2016, revealing the migratory characteristics and patterns of high-tech enterprises within the metropolitan area by using spatial analysis methods and mathematical statistics. The results show that: (1) Migrating enterprises are mainly mixed-ownership and foreign-funded or privately-owned enterprises. Medium-sized enterprises are more likely to relocate than small and large enterprises, especially in lucrative sectors. (2) The migration of high-tech enterprises is mainly based on proximity migration. More than 77.19% of enterprises tend to move to agglomerations or agglomeration expansion areas such as Jiuxianqiao and the Beijing Economic-Technological Development Area (BDA) in Daxing District. In addition, 33 enterprises experience varying degrees of scale expansion after relocation. (3) Most high-tech manufacturing enterprises are in the growth stage of their life cycle. They initially relocate between 7 and 16 years after their establishment, which means that there is roughly a 7-year adaptation or development period for firms within a region after their establishment. During the adaptation period, companies do not tend to relocate; afterwards, as they grow, they need to find other spaces in which to do so. For reasons such as familiarity with the environment, enterprises give priority to finding new locations within the agglomeration and the surrounding area. These results deepen the understanding of the temporal and spatial characteristics of the migration of enterprises within the city from the microscopic perspective and provide a scientific basis for the optimization of industrial space in a metropolis.

Impact of urban green space on self-rated health: Evidence from Beijing.

As a crucial component of urban green space (UGS), urban parks have been found to be closely associated with the health of urban residents. Drawing on a large-scale survey, “International First-class Harmonious and Livable Capital”, in Beijing conducted in 2018, this paper examines the impact of subjective and objective characteristics of UGS on residents' self-rated health (SRH) by using a binary logistic regression model. The results indicate that the overall SRH status of urban residents in Beijing is relatively good, with 73.8% of the respondents reporting good SRH. The perceived quality of UGS and objectively measured accessibility to UGS are positively associated with residents' SRH, but the subjective indicator of UGS has a greater impact on SRH than the objective indicator of UGS. In terms of influencing mechanisms, social interaction and air quality perception were the two major mediators of UGS that affected residents' SRH. The heterogeneity analysis suggests that objective accessibility to different types of urban parks has mixed effects on residents' SRH. Access to high-quality parks is positively associated with residents' SRH, whereas access to common parks has a negative impact on residents' SRH. Our findings provide important policy implications for optimizing urban park design and improving the quality of urban park provision according to human needs in the Beijing Metropolitan Area.

An alternative method of developing landscape strategies for urban cooling: A threshold-based perspective

Increasing the amount of blue-green spaces has been recognized as an effective strategy to mitigate the urban heat island (UHI) effect. Specific quantitative and actionable landscape mitigation strategies for different land-use and land-cover (LULC) types to cool down cities, have been rarely addressed. In this paper, the absolute threshold of cooling (ToCabs) —the specific threshold of a certain influencing factor to ensure the effective cooling of a particular patch type, was proposed and defined for the first time to facilitate the research. The effects of size, shape and neighboring greenspace percentage (NGP) of different types of LULC patches on land surface temperature (LST) were examined and their corresponding ToCabs values were determined if existed, in the metropolitan areas of Beijing, Shanghai and Tianjin using satellite images. Results demonstrate that larger-sized and complex-shaped water bodies and forested areas produce greater cooling effect. The size is irrelevant to the cooling intensity of grass-dominated patches. ToCabs of patch size exist and were identified for water bodies (Beijing, 5 ha; Shanghai, 10 ha; and Tianjin, 5 ha) and forested areas (Beijing, 20 ha; Shanghai, 50 ha; and Tianjin, 20 ha). Besides, NGP is negatively correlated with patch LST for all LULC types, indicating that increasing the amount of neighboring vegetation cover can effectively lower the nearby patch LST. ToCabs of NGP exist and were identified for water bodies (Beijing, 30.3%; Shanghai, 61.6%; and Tianjin, 20%) and grass-dominated areas (Beijing, 89.1%; Shanghai, 76.9%; and Tianjin, 81.5%). Among the three influencing factors, the size accounts for greatest variability in patch LST for forested areas and impervious surfaces, and neighboring greenspace proportion accounts for the most variability in LST for water bodies and grass-dominated areas. The methodology and results of this study could help to orientate specific and actionable landscape strategies of urban cooling.

Green Space Cooling Effect and Contribution to Mitigate Heat Island Effect of Surrounding Communities in Beijing Metropolitan Area.

With the rapid process of urbanization and global warming, many metropolises are vulnerable to high temperatures in summer, threatening the health of residents. However, green spaces can generate a cooling effect to mitigate the urban heat island effect in big cities. They can also help to improve the living quality and wellbeing of surrounding residents. In this paper, we utilized the radiative transfer equation algorithm, k-means clustering algorithm, big data crawling, and spatial analysis to quantify and map the spatial distribution, cooling capacity, and cooling contribution for surrounding communities of 1,157 green spaces within Beijing Fifth Ring Road, a typical metropolitan area. The findings showed that (1) the area proportion of the heat island in the study area is larger than that of the cooling island. Accounting for only about 30% area in the study area, the green spaces reduce the average land surface temperature by 1.32°C. (2) The spatial features of green space, such as area and shape complexity, have a significant influence on its cooling effect. (3) Four clusters of green spaces with specific spatial features and cooling capacity were identified. And there were differences among these clusters in green space cooling contribution for the surrounding communities. (4) The differences in green space cooling contribution also existed in different urban zones. Specifically, the middle zone performed significantly better than the inner and outer zones. (5) We furthered in finding that some green spaces with medium and high cooling contributions need to improve their cooling capacity soon, and some green spaces with low cooling contributions or no contributions have a good potential for constructing new communities in the future. Our study could help planners and government understand the current cooling condition of green spaces, to improve their cooling capacity, mitigate the urban heat island effect, and create a comfortable and healthy thermal environment in summer.

Impact of the COVID-2019 Pandemic on a New Type of Urbanization in China: an Analysis of the Transformation of the Lives of Large Cities Residents (the Саse of Shanghai, Beijing and Guangzhou)

The study is aimed at identifying the features of the impact of the COVID-2019 pandemic on urbanization in China. The objects of the study were the cities of Shanghai, Beijing and Guangzhou, which is determined by their representativeness and the possibility of disseminating the results of the study to other large urban agglomerations in China. The methodology is based on an integrated methodological approach that combines elements of descriptive statistical and sociological research. To assess the relationship between the spread of the pandemic in the Chinese metropolitan areas of Shanghai, Beijing and Guangzhou, correlation and regression analysis was used. The study of the impact of the pandemic on the quality of life of the urban population was carried out through a survey using a structured questionnaire. The data obtained were processed and analyzed using the SPSS 26.0 software tools, which made it possible to identify the degree of connection between urbanization and various aspects of the life of urban people, including their employment, well-being, access to health services, and the transformation of life values. It was found that there is a strong link between the level of urbanization and the pandemic, therefore, with the development of urbanization, the number of cases of infection (if other conditions remain unchanged) increases, and vice versa. As a result of a survey of urban residents, an assessment of the frequencies and correlations of responses, it was revealed that the impact of the pandemic on the daily life of urban people was most pronounced on the level of well-being of informally employed people, increasing the availability of health care, and only then on the values of citizens. These results should be taken into account in the formation of directions for improving the quality of urbanization.

Resident travel mode prediction model in Beijing metropolitan area.

With the development of economic integration, Beijing has become more closely connected with surrounding areas, which gradually formed the Beijing metropolitan area (BMA). The authors define the scope of BMA from two dimensions of space and time. BMA is determined to be the built-up area of Beijing and its surrounding 10 districts. Designed questionnaire survey the personal characteristics, family characteristics, and travel characteristics of residents from 10 districts in the surrounding BMA. The statistical analysis of questionnaires shows that the supply of public transportation is insufficient and cannot meet traffic demand. Further, the travel mode prediction model of Softmax regression machine learning algorithm for BMA (SRBM) is established. To further verify the prediction performance of the proposed model, the Multinomial Logit Model (MNL) and Support Vector Machine (SVM), model are introduced to compare the prediction accuracy. The results show that the constructed SRBM model exhibits high prediction accuracy, with an average accuracy of 88.35%, which is 2.83% and 18.11% higher than the SVM and MNL models, respectively. This article provides new ideas for the prediction of travel modes in the Beijing metropolitan area.

High‐Resolution Land Surface Modeling of the Effect of Long‐Term Urbanization on Hydrothermal Changes Over Beijing Metropolitan Area

AbstractThe impacts of urbanization on terrestrial thermal and hydrological changes should be considered for sustainable development of urban environment and economy. However, previous studies mainly focused on the impacts at short time scales, which is insufficient to provide the needed information. This study augmented the high‐resolution Conjunctive Surface‐Subsurface Process version 2 (CSSPv2) land model with an urban canopy model that incorporates domestic wastewater recharge (DWWR) and dynamical impervious surface area (ISA) to develop a new CSSPv2_Urban model and conducted 38‐year simulations over the Beijing metropolitan area at 1 km resolution to quantify the urbanization impacts. Compared with CSSPv2, CSSPv2_Urban decreases the root‐mean‐square errors in surface latent/sensible heat fluxes, near‐surface air temperature and land surface temperature by 15%–66%, increases Kling‐Gupta efficiency for streamflow by 27%–34%, and reproduces the observed long‐term trends of land surface temperature and streamflow. It also reduces the corresponding errors in the modern reanalysis products by up to 70%. The model sensitivity analyses showed that the urbanization over Beijing area contributed to 31% of the surface warming during 1979–2017 and kept the warming trend over the new development zone during 2000–2013 when global warming slowed down. Although the large fraction of ISA in the city center increased streamflow by generating more surface runoff, 39%–62% of the increasing trend of streamflow was caused by the increased DWWR. Our study highlights the importance of the urban heat island effect from a climatological perspective and suggests that the influences of urbanization on streamflow may be underestimated if ignoring DWWR.

Addressing the macroeconomic and hedonic determinants of housing prices in Beijing Metropolitan Area, China

This paper examined both the macroeconomic and hedonic determinants of housing prices in Beijing. The vector autoregression (VAR) model was used to explain the dynamic impacts of macroeconomic indicators on housing prices, and the geographical weighted regression (GWR) model was used to reveal the spatial variations in the effects of hedonic attributes on housing prices. The VAR results suggest that shocks to the macroeconomic indicators produce different dynamic changes in housing prices. Both an increase in the money supply and a decrease in the mortgage rates lead a rise in housing prices in the long term. Therefore, monetary policies and mortgage rates could be used as long-term financial tools to regulate the dynamics of housing prices. The GWR results show that the effects of hedonic attributes vary spatially. The size of a house has a negative impact on housing prices, especially in the downtown areas. The age of a house also has a negative impact on housing prices, especially in the suburbs. The effect of the number of bedrooms changes from positive in the downtown areas to negative in the suburbs. Furthermore, convenient accessibilities to critical amenities such as commercial centers, metro stations and high-quality schools contribute greatly to high housing prices in the downtown areas. The hedonic analysis of housing prices provides great implications for forming policy on urban planning, building design and public services construction. The core problem concerning urban planning, building design and public services construction in Beijing is the mismatch between working and living spaces. Therefore, relevant policy makings should focus on reducing the mismatch between working and living spaces, which is conductive to alleviating the spatial imbalance between housing supply and demand, narrowing the development gap within the city, and solving the urban diseases. Therefore, one important policy suggestion is that urban planning and public services construction should be upgraded to introduce more governmental offices, job opportunities, high-quality schools, commercial facilities and infrastructures into the suburbs. Another important policy suggestion is that the design for the floor area ratio and internal structures of new residential buildings should be planned to accommodate more residents and form better living environment in the downtown areas. Those suggestions can provide some guidance for solving urban problems in China and in other countries.

Enhancement of Cloud-to-Ground Lightning Activity Caused by the Urban Effect: A Case Study in the Beijing Metropolitan Area

To investigate the possible impact of urban development on lightning activity, an eight-year (2010–2017) cloud-to-ground (CG) lightning dataset provided by the National-Wide Lightning Detection Network in China was analyzed to characterize the CG lightning activity in the metropolitan area of Beijing. There is a high CG flash density area over the downtown of Beijing, but different from previous studies, the downwind area of Beijing is not significantly enhanced. Compared with the upwind area, the CG flash density in the downtown area was enhanced by about 50%. Negative CG flashes mainly occurred in the downtown and industrial area, while positive CG flashes were distributed evenly. The percentage of positive CG flashes with Ipeak ≥ 75 kA is more than six times that of the corresponding negative CG flashes in the Beijing area. The enhancement of lightning activity varies with season and time. About 98% of CG flashes occurred from May to September, and the peak of CG diurnal variation is from 1900 to 2100 local time. Based on the analysis of thunderstorm types in Beijing, it is considered that the abnormal lightning activity is mainly responsible for an enhancement of the discharge number in frontal systems rather than the increase of the number of local thunderstorms. In addition, there is a non-linear relationship between pollutant concentrations and CG flash number, which indicates that there are other critical factors affecting the production of lightning.

Travel preferences of multimodal transport systems in emerging markets: The case of Beijing

Abstract Metropolises in emerging markets are facing serious urban transport challenges. Understanding people’s travel preferences is crucial for designing effective sustainable urban policies. Little attention has been paid to studying travel preferences in multimodal transport systems in these markets. This study estimates the travel preferences in the metropolitan area of Beijing, which is notoriously plagued with high degrees of congestion. We administered a series of interwoven stated preference experiments on travel behavior. A semi-random sample of 2652 respondents participated in the experiments. The data were pooled and a scaled mixed logit model was used for estimation. The results provide rich information on how trade-offs are made among the manipulated attributes regarding travel time, cost, convenience, and reliability. Many findings deviate from results obtained in developed countries. A contrast standing out is that travelers in Beijing place much less weight on possible delays caused by traffic congestion.

중국 수도권의 산업구조 분석 : 전·후방연계효과를 중심으로

2000년대 중반 이후, 중국은 산업구조 고도화와 산업연계 강화를 통한 수도권의 산업발전을 도모해 오고 있다. 이에 본 연구는 중국 수도권을 구성하고 있는 베이징, 톈진, 허베이의 산업연관모형을 구축하고, 이를 통해 각 지역의 전·후방 산업연계구조를 분석하였다. 각 지역별로 전·후방 산업연계효과가 큰 산업들을 도출하고 비교분석하였으며, 중국 수도권 내 지역별 핵심산업을 파악하고, 각 지역의 산업구조 고도화와 지역별 산업연계 가능성에 대해 검토하였다. 분석결과, 베이징과 톈진의 전후방 연계가 강한 핵심산업들은 대부분 산업구조 고도화 목표와의 관련성이 낮은 것으로 나타났다. 반면, 허베이의 경우 전후방 연계가 강한 핵심산업 대부분이 산업구조 고도화 목표와 관련된 것으로 나타났다.BR 이를 통해 얻을 수 있는 정책적 시사점은 다음과 같다. 베이징과 톈진의 경우, 산업구조의 고도화 목표를 달성하기 위한 핵심산업 분야를 조속히 육성하고, 타 산업부문과의 연계구조를 형성하기 위한 정책방안을 강구할 필요가 있다. 허베이의 경우, 성공적인 산업구조 고도화를 위해서 베이징, 톈진과 유기적으로 연계되는 산업이전계획을 수립, 실행할 필요가 있다. 국내 수도권의 경우 지역별 산업기능 중복문제를 해결하기 위한 산업구조 재편이 이뤄져야 한다. 이를 위해 수도권의 지방정부들이 주도적으로 협력하여 상호보완성을 담보하는 지역산업계획을 수립, 추진하는 방안을 검토해야 한다.

Quantile Regression Based Methods for Investigating Rainfall Trends Associated with Flooding and Drought Conditions

Conducting trend analysis of climatic variables is one of the key steps in many climate change impact studies where trend is often checked against aggregated variables. However, there is also a strong need to investigate the trend of the data in different regimes – examples include high flow versus low flow, and heavy precipitation versus prolonged dry period. For this matter, quantile regression (QR) based methods are preferred as they can reveal the temporal dependencies of the variable in question for not only the mean value, but also its quantiles. As such, the tendencies revealed by the QR methods are more informative and helpful in studies where different mitigation methods need to be considered at different severity levels.In this paper, we demonstrate the use of several quantile regressions methods to analyse the long-term trend of rainfall records in two climatically different regions: The Dee River catchment in the United Kingdom, for which daily rainfall data of 1970–2004 are available; and the Beijing Metropolitan Area in China for which monthly rainfall data from 1950 to 2012 are available. Two quantiles are used to represent heavy rainfall condition (0.98 quantile) and severe dry condition (0.02 quantile). The trends of these two quantiles are then estimated using linear quantile regression before being spatially interpolated to demonstrate their spatial distribution (for Dee river only). The method is also compared with traditional indices such as SPI. The results show that the quantile regression method can reveal patterns for both extremely wet and dry conditions of the areas. The clear difference between trends at the chosen quantiles manifests the utility of QR in this context.

Measuring Public Transit Accessibility Based On Google Direction API

Background: Accessibility is considered as an important indicator for the public transit service level. Transit accessibility is generally evaluated by its distance to transit access points such as bus stops and metro stations, and methods of measuring the access distances to transit points have been relying heavily on geographic data of transit facilities, built environments and pedestrian routes. Data collecting and analyzing are tedious for researchers in conventional approaches. As the application of cloud computing is on the rise, open services provided by Google Cloud Platform may simplify the procedure of accessibility measurement if the outputs of the open computing services could be validated. Aims and Objectives: This paper aims to develop a method of measuring public transit accessibility based on Google Direction API rather than local data analyzing. A mechanism of API (Application Program Interface) probing is introduced. In a case study, the metropolitan area of Beijing was sliced into gridded spaces, with transit access distance of each cell space calculated by Google Direction API. The access distances in the API feedbacks were compared with transit access numbers in each cell area in order to validate the method with their correlation coefficient. Results and Conclusion: It was found that Google Direction API generally gave shorter access distances in cell areas with more public access points. The conclusion is that open cloud services such as Google Direction API may serve as alternative solutions to public transit accessibility measurement. Transit researchers and agencies may take advantage of such open API services to avoid the tediousness of collecting and processing geographic data sets on transit facilities.