Homogeneous Zones Research Articles

An integrated hierarchical classification and machine learning approach for mapping land use and land cover in complex social-ecological systems

Mapping land use and land cover (LULC) using remote sensing is fundamental to environmental monitoring, spatial planning and characterising drivers of change in landscapes. We develop a new, general and versatile approach for mapping LULC in landscapes with relatively gradual transition between LULC categories such as African savannas. The approach integrates a well-tested hierarchical classification system with the computationally efficient random forest (RF) classifier and produces detailed, accurate and consistent classification of structural vegetation heterogeneity and density and anthropogenic land use. We use Landsat 8 OLI imagery to illustrate this approach for the Extended Greater Masai Mara Ecosystem (EGMME) in southwestern Kenya. We stratified the landscape into eight relatively homogeneous zones, systematically inspected the imagery and randomly allocated 1,697 training sites, 556 of which were ground-truthed, proportionately to the area of each zone. We directly assessed the accuracy of the visually classified image. Accuracy was high and averaged 88.1% (80.5%–91.7%) across all the zones and 89.1% (50%–100%) across all the classes. We applied the RF classifier to randomly selected samples from the original training dataset, separately for each zone and the EGMME. We evaluated the overall and class-specific accuracy and computational efficiency using the Out-of-Bag (OOB) error. Overall accuracy (79.3%–97.4%) varied across zones but was higher whereas the class-specific accuracy (25.4%–98.1%) was lower than that for the EGMME (80.2%). The hierarchical classifier identified 35 LULC classes which we aggregated into 18 intermediate mosaics and further into five more general categories. The open grassed shrubland (21.8%), sparse shrubbed grassland (10.4%) and small-scale cultivation (13.3%) dominated at the detailed level, grassed shrubland (31.9%) and shrubbed grassland (28.9%) at the intermediate level, and grassland (35.7%), shrubland (35.3%) and woodland (12.5%) at the general level. Our granular LULC map for the EGMME is sufficiently accurate for important practical purposes such as land use spatial planning, habitat suitability assessment and temporal change detection. The extensive ground-truthing data, sample site photos and classified maps can contribute to wider validation efforts at regional to global scales.

Summarizing soil chemical variables into homogeneous management zones – case study in a specialty coffee crop

Homogeneous management zones (HMZs) delineation is important for the application of precision agriculture because farm management decisions are based on it. Diverse soil chemical characteristics are important for the HMZs delineation. However, summarizing several variables into homogeneous zoning while taking into account the spatial distribution pattern of soil chemical characteristics is a challenge. Addressing this challenge is important to produce HMZs oriented for practical use for the farmers. In this work, 17 soil chemical variables were jointly analyzed for HMZ delineation by using indicator kriging (IK) to interpolate a soil fertility index (SFI). Soil samples were taken from a 4.5 ha area in a quasi-regular grid at 0 - 0.20 m depths in November 2019 (66 samples) and May 2021 (40 samples). Soil P, K, Ca, Mg, S, Na, H, Mn, Fe, Zn, B, cation exchange capacity, aluminum saturation, total organic carbon, base saturation, and organic matter were analyzed. In May 2021 the coffee yield was sampled together with the soil. Applying the SFI and then interpolating it using IK were effective for summarizing soil chemical variables into binary HMZs, showing a zone with higher priority for fertilization (therefore, lower general soil fertility) and another zone with low priority for fertilization. The summarizing process of several variables into binary HMZ was validated by evaluating the boxplots of each variable in each HMZ. Also, higher soil fertility areas presented a higher average coffee yield. Results indicated that joint use of SFI and IK was adequate to delineate HMZs in terms of summarizing soil fertility and separating coffee yield average variability. Delineating management zones by using the SFI approach is flexible for relatively limited sampled studies (less than one hundred samples) where machine learning and geostatistical methods may fail for lack of data.

Statistical downscaling of high-resolution precipitation in India using convolutional long short-term memory networks

Abstract Statistical downscaling of the General Circulation Model (GCM) simulations are widely used for accessing climate changes in the future at different spatiotemporal scales. This study proposes a novel Statistical Downscaling (SD) model established on the Convolutional Long Short-Term Memory (ConvLSTM) Network. The methodology is applied to obtain future projection of rainfall at 0.25° spatial resolution over the Indian sub-continental region. The traditional multisite downscaling models typically perform downscaling on a single homogeneous rainfall zone, predicting rainfall at only one grid point in a single model run. The proposed model captures spatiotemporal dependencies in multisite local rainfall and predicts rainfall for the entire zone in a single model run. The study proposes a Shared ConvLSTM model providing a single end-to-end supervised model for predicting the future precipitation for entire India. The model captures the regional variability in rainfall better than a region-wise trained model. The projected future rainfall for different scenarios of climate change reveals an overall increase in the rainfall mean and spatially non-uniform changes in future rainfall extremes over India. The results highlight the importance of conducting in-depth hydrologic studies for different river basins of the country for future water availability assessment and making water resource policies.

Crystal varizonicity: fundamental and applied aspects.

A set of ideas about the homogeneous variable zone structure in the near-surface region of ionic crystals is presented. Its origin is shown using generalizations of the Seitz–Madelung scheme. A number of important consequences have been demonstrated: the peculiarity of local surface levels different from Tamm levels; increase in the critical temperature of superconductivity in a sandwich structure; a significant anti-degradation role of these levels in the processes of energy exchange at the interfaces. It is concluded that such homo varizonality is an excellent example of the implementation of the “Bonds and Bands” concept.

Agro-climatic Update of Favorable Areas to the Cultivation of Robusta and Arabusta Coffee in Côte d'Ivoire

Following a drastic fall in coffee production, Côte d'Ivoire has made it a priority to revive its coffee-growing industry. This requires the development of regionalized technical itineraries adapted to climate change. To this end, agro-climatic analyzes were carried out using data from 55 rain gauge stations covering the period from 1985 to 2019. This involved cross-referencing the climatic parameters calculated, taking into account the climatic requirements of coffee trees (Robusta and Arabusta) to define homogeneous agro-climatic zones in the forest and pre-forest zones bounded by the 8th parallel north. The results show that 70.47% of the area is suitable for growing Robusta coffee, compared with 11.37% for Arabusta. The mountainous west remains generally favorable to growing both varieties of coffee. The study also updated information on the agro-climatic suitability of coffee-growing areas.

Land use suitability analysis using an approach combining GIS, regionalization, and PROMETHEE II

ABSTRACT Land-use Suitability Analysis, which aims to identify the suitability of a given area for a particular use by using a certain number of criteria, is a domain that has received a lot of attention from researchers during the last 20 years and most of the works that have been proposed rely on the use of multi-criteria methods which are based on a complete aggregation of alternatives such as Weighted Linear Combination (WLC). The outranking methods, which are multi-criteria methods that rely on a partial aggregation of alternatives, have rarely been used. In the context of Land-use Suitability Analysis, outranking methods quickly reach their computational limits because they require performing comparisons between each pair of pixels. To reduce the initial number of alternatives, we propose to proceed to the regionalization of the study area by grouping neighbouring pixels that have similar properties in homogeneous zones. We then use PROMETHEE II (Preference Ranking Organization Method for Enrichment of Evaluations), an outranking method whose objective is to rank a set of alternatives from the best to the worst. A study for the choice of a landfill site was carried out using our approach and the obtained results were compared with those of WLC.

Irrigation Zone Delineation by Coupling Neural Networks with Spatial Statistics

Variable rate irrigation (VRI) seeks to increase the efficiency of irrigation by spatially adjusting water output within an agricultural field. Central to the success of VRI technology is establishing homogeneous irrigation zones. In this research, we propose a fusion of statistical modeling and deep learning by using artificial neural networks to map irrigation zones from simple-to-measure predictors. We further couple our neural network model with spatial correlation to capture smooth variations in the irrigation zones. We demonstrate the effectiveness of our model to define irrigation zones for a farm of winter wheat crop in Rexburg, Idaho.

Precipitation Scaling in Extreme Rainfall Events and the Implications for Future Indian Monsoon: Analysis of High‐Resolution Global Climate Model Simulations

AbstractThe increase in water holding capacity of the atmosphere with temperature, given by the Clausius‐Clapeyron (CC) relationship, describes the changes in extreme rainfall intensities at warmer atmospheric states. We study the characteristics of extreme rainfall events (EREs) during the Indian summer monsoon season with respect to thermodynamic changes and precipitation‐scaling over the Indian subcontinent and its homogeneous rainfall zones. We utilize outputs from a present‐day climate simulation and a time‐slice future climate change projection experiments of a high‐resolution global climate model. Large changes are seen for very EREs (vEREs) which suggests their sensitivity to warmer temperatures. In future, the altered radiative forcing will heat up the upper atmosphere, stabilize it and offset the effect of increasing humidity on precipitation intensity. Our analysis also suggests that more convective clouds and the interplay of increased moisture content and circulation will result in future changes in EREs.

Mapping soil organic carbon in northern France using adaptive zoning regression kriging based on LUCAS dataset

To improve the accuracy of soil organic carbon (SOC) mapping, an adaptive zoning regression kriging (AZ_RK) framework was proposed, which simultaneously considers spatial heterogeneity and the influence of multiple environmental variables. In AZ_RK, the study area was first adaptively divided into several spatially homogeneous zones. Then, the SOC distribution was mapped in each zone separately considering the relationships between SOC and environmental variables. The results indicated that the influence of environmental variables on SOC in different zones differed. The proposed zoning strategy was proven to be valid, which can account for 36.6% of the spatial variance in SOC. Furthermore, AZ_RK outperformed other methods with the lowest RMSE and the highest R 2 ( R cv 2 = 0.495 and R p 2 = 0.481). We concluded that AZ_RK can effectively cluster spatially homogeneous zones and adaptively map SOC with high estimation accuracy, which can serve as a valuable model reference for precise fertilization in regions characterized by spatial heterogeneity.

On drilling speed of London Clay from MWD data with time-series algorithm for ground characterisation

The ground conditions around an old tunnel in London Clay strata were extensively studied. The correlation between ‘measurement while drilling’ (MWD) drilling parameters and drillhole investigation results, however, was not satisfactory due to the significant noise in the drilling speed. In this paper the original MWD data published in the literature are re-examined and the associated noise issue is addressed by transferring the data in depth series into the data in time series. The curve of the drill-bit advancement depth with net drilling time can be visually divided into many linear segments. Each linear segment has a gradient or constant drilling speed and represents a homogeneous geomaterial zone with constant resistance to drill-bit loading. The depth variation of the constant drilling speed zones is further compared and well verified with the depth variation of lithological strata units identified from rotary-cored samples. A strength grade system is also proposed based on the linear zoning results. A new rock quality designation is calculated from the thickness of the zone of constant drilling speed for the London site and is compared with those at four other sites; this further enhances the understanding and confidence in the linear zoning result and the constant drilling speeds as resistant strengths associated with the lithological strata units in London Clay.

Soil fertility shapes fire activity across Mediterranean‐type climate regions

AbstractAimTo quantify the role of soil fertility in the spatial variability of fire activity and to identify the mechanisms that drive this variability.LocationThe five Mediterranean‐type climate regions of the world.Time Period2002–present.Major Taxa StudiedTerrestrial plants.MethodsWe compiled remotely sensed data on fire activity, climate, net primary productivity and chemical soil properties for bioclimatically homogeneous zones within the five Mediterranean‐type climate regions of the world. Putative direct and indirect effects of the environmental variables on fire activity were evaluated through structural equation modelling.ResultsFire activity increased with net primary productivity, as expected for ecosystems with fuel‐limited fire regimes. Soil acidity and the concentration of exchangeable aluminium also increased fire activity, supporting the idea that low fertility promotes plant characteristics that favour fire initiation and spread.Main ConclusionsOur research supports a positive relationship between wildfires and low soil fertility in Mediterranean‐type climate regions across the globe. Therefore, soil fertility should be incorporated into models predicting future fires in a warming world.

Automatic Crop Classification Based on Optimized Spectral and Textural Indexes Considering Spatial Heterogeneity

Crop recognition with high accuracy at a large scale is hampered by the spatial heterogeneity of crop growth characteristics under the complex influence of environmental conditions. With the aim to automatically realize large-scale crop classification with high accuracy, this study proposes an automatic crop classification strategy considering spatial heterogeneity (ACCSH) by combining the geographic detector technique, random forest average accuracy model, and random forest classification model. In ACCSH, spectral and textural indexes that can quantify crop growth characteristics and environmental variables with potential driving effects are first calculated. Next, an adaptive spatial heterogeneity mining method based on the geographic detector technique is proposed to mine spatial homogeneous zones adaptively with significant differentiation of crop growth characteristics. Subsequently, in view of the differences in crop growth characteristics and key classification indexes between spatial homogeneous zones, correlation analysis, and random forest average accuracy are combined to optimize classification indexes independently within each zone. Finally, random forest is used to classify the target crop in each spatial homogeneous zone separately. The proposed ACCSH is applied to automatically recognize crop types, specifically wheat and corn, in northern France. Results show that kappa coefficients of wheat and corn using ACCSH are 15% and 26% higher than those of classifications at the global scale, respectively. In addition, the index optimization strategy in ACCSH shows apparent superiority. Kappa coefficients of wheat and corn are 5–18% and 9–42% higher than those of classifications based on non-optimized indexes, respectively. In general, ACCSH can automatically realize crop classification with a high precision that suggests its reliability.

Seasonality of Biophysical Parameters in Extreme Years of Precipitation in Pernambuco: Relations, Regionalities, and Variability

This study analyzed the seasonality of biophysical parameters in the extreme years of precipitation and the relationship with the monthly precipitation of the state of Pernambuco at the regional level (Pernambuco) and homogeneous precipitation zones: zone 1—semiarid, zone 2—transition and zone 3—coastal. For this, the biophysical parameters at the monthly level in the extreme years, 2004 (wet) and 2012 (dry) were related to precipitation data of 45 rainfall stations. Using the Google Earth Engine platform, we calculate the biophysical parameters with MODIS products: Albedo, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil Adjusted Vegetation Index (SAVI), Normalized Difference Water Index (NDWI) and surface temperature (ST). Considering the most critical period, between September and December, of a wet year (2004) with a dry year (2012), there is an average reduction of 14% of vegetation indices (NDVI, EVI and SAVI), a 60% reduction in NDWI, an increase of 4% in albedo and 3% in surface temperature. For monitoring the water conditions of the state of Pernambuco, the most appropriate biophysical parameter is the NDWI index and surface temperature. In addition to NDWI, it is recommended to use EVI for semiarid areas (zone 1) and ST for coastal areas (Zones 2 and 3).

JRA55 is the best reanalysis representing observed near-surface wind speeds over India

Reanalyses are often regarded as the most reliable datasets that give a comprehensive picture of meteorological parameters all over the globe. They are the first choice of dataset for researchers interested in analysing meteorological variables. Recognizing their importance, several agencies worldwide produce reanalysis datasets using different methods. This study aims to reduce the uncertainty associated with choosing among these reanalyses by identifying the best reanalysis representing the near-surface wind speeds over India. For this purpose, 10 m wind speed data from six reanalyses are used: National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis (NCEP1), National Centers for Environmental Prediction-Department of Energy reanalysis (NCEP2), Japanese 55-year Reanalysis (JRA55), Indian Monsoon Data Assimilation and Analysis (IMDAA), European Centre for Medium-Range Weather Forecasts reanalysis version 5 (ERA5), and Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA2). The evaluation is done by comparing the spatially-averaged reanalyses against the station observations obtained from National Centers for Environmental Information (NCEI) over seven homogenous climate zones of India for the 1980–2020 period. Results demonstrate wide divergence between the reanalyses and observations in terms of monthly mean statistics, inter-annual variability (IAV), and annual mean trend. Despite the differences, JRA55 best represents the trends, IAV, and shows the closest resemblance to observations. On an annual scale, the 10 m wind speed observations over India have a weak correlation with the Nino 3.4 index; however, they have a significant negative correlation with the Dipole Mode index (DMI), implying that SST anomalies over the Indian Ocean have a considerable impact on the weather and climate patterns of the Indian subcontinent. JRA55 closely reflects these relationships with large-scale ocean-atmospheric features as well. Overall, we conclude that JRA55 can be considered a proxy for 10 m wind speed observations over India.

Using a Non-Contact Sensor to Delineate Management Zones in Vineyards and Validation with the Rasch Model.

The production of high-quality wines is one of the primary goals of modern oenology. In this regard, it is known that the potential quality of a wine begins to be determined in the vineyard, where the quality of the grape, initially, and later that of the wine, will be influenced by the soil properties. Given the spatial variability of the fundamental soil properties related to the potential grape production, such as texture, soil organic matter content, or cation exchange capacity, it seems that a uniform management of a vineyard is not the most optimal way to achieve higher grape quality. In this sense, the delineation of zones with similar soil characteristics to implement site-specific management is essential, reinforcing the interest in incorporating technologies and methods to determine these homogeneous zones. A case study was conducted in a 3.3 ha vineyard located near Évora, south of Portugal. A non-contact sensor (DUALEM 1S) was used to measure soil apparent electrical conductivity (ECa) in the vineyard, and later, a kriged ECa map was generated. ECa and elevation maps were utilised to delineate homogeneous zones (management zones, MZs) in the field through a clustering process. MZs were validated using some soil properties (texture; pH; organic matter-OM; phosphorous-P2O5; potassium-K2O; the sum of the exchange bases-SEB; and cation exchange capacity-CEC), which were determined from 20 soil samples taken in the different MZs. Validation was also performed using Rasch measures, which were defined based on the formulation of the objective and probabilistic Rasch model, integrating the information from the aforementioned soil properties at each sampling location. The comparison of the MZs was more evident with the use of the Rasch model, as only one value was to be employed in each MZ. Finally, an additional validation was conducted using a vegetation index to consider the plant response, which was different in each MZ. The use of a non-contact sensor to measure ECa constitutes an efficient technological tool for implementing site-specific management in viticulture, which allows for the improvement of decision-making processes by considering the inherent spatial variability of the soil.

Generic optimization approach of soil hydraulic parameters for site-specific model applications

Site-specific crop management is based on the postulate of varying soil and crop requirements in a field. Therefore, a field is separated into homogenous management zones, using available data to adapt management practices environment to maximize productivity and profitability while reducing environmental impacts. Due to advancing sensor technologies, crop growth and yield data on more minor scales are common, but soil data often needs to be more appropriate. Crop growth models have shown promise as a decision support tool for site-specific farming. The Decision Support System for Agrotechnology Transfer (DSSAT) is a widely used point-based model. To overcome the problem of inappropriate soil input data problem, this study introduces an external plug-in program called Soil Profile Optimizer (SPO), which uses the current DSSAT v4.8 to calibrate soil profile parameters on a site-specific level. Developed as an inverse modelling approach, the SPO can calibrate selected soil profile parameters by targeting available in-season plant data. Root Mean Square Error (RMSE) and normalized RMSE as error minimization criteria are used. The SPO was tested and evaluated by comparing different simulation scenarios in a case study of a 3-yr field trial with maize. The scenario with optimized soil profiles, conducted with the SPO, resulted in an R2 of 0.76 between simulated and observed yield and led to significant improvements compared to the scenario conducted with field scale soil profile information (R2 0.03). The SPO showed promise in using spatial plant measurements to estimate management zone scale soil parameters required for the DSSAT model.

Evaluation of global precipitation products for meteorological drought assessment with respect to IMD station datasets over India

In-situ precipitation data is the most reliable data for the drought assessment. However, gridded datasets are commonly used due to limited availability of station data. However, the gridded data may exhibit bias compared to station data. The present study has been undertaken to examine the performance of gridded datasets from global precipitation products (gauge-interpolated, merged from various sources, re-analysis and satellite products) for the assessment of meteorological drought with reference to station datasets of the India Meteorological Department (IMD) from 2000 to 2019 at six drought zones over India. Various statistical metrics were used to check the performance of the precipitation products at four seasons of India. The Standardised Precipitation Index (SPI) was used at a 1-month timescale to evaluate the meteorological drought characteristics. The gridded precipitation products, in regions of high rainfall, tended to underestimate precipitation during the monsoon season and overestimate it during the non-monsoon season. When precipitation was analysed spatially using statistical parameters, the product Integrated Multi-Satellite Retrievals (IMERG Final-Run) outperformed all the other precipitation products in the four seasons and six drought zones over India. For drought characteristics evaluation, the products Climate Prediction Center (CPC) Morphing Technique (CMORPH) and Global Precipitation Climatology Centre (GPCC) outperformed all the other products in the six drought zones over India. Further, in order to rank the precipitation products, the Spearman rank correlation coefficient (SCC) and critical success index (CSI) were used. The analysis results showed that IMERG Final-Run, GPCC and Multi-Source Weighted-Ensemble Precipitation (MSWEP-PastNoGauge) ranked best out of all the precipitation products when analysed for drought monitoring and assessment at six drought zones over India. Overall, IMERG Final-Run outperformed all the other precipitation products for drought monitoring, while TerraClimate performed poorly in most of the drought zones and in India as a whole. The study's findings will help choose suitable gridded precipitation dataset for meteorological drought assessment across six homogeneous drought zones and in India as a whole, aiding operational drought risk management and early warnings.

Priorización de cuencas hidrográficas para la conservación del suelo y el agua basado en las técnicas GIS, PCA y WSA

Soil and water conservation is a priority in the watersheds of arid and semi-arid regions for the proper planning and integrated management of water resources. The objective of this work is prioritizing 91 watersheds in 14 regions of Peru with different geographical, hydrological and geological conditions, through the integration of Geographic Information Systems (GIS), Principal Component Analysis (PCA) and the Weighted Sum Approach (WSA). In addition, homogeneous regions were identified with hierarchical Cluster Analysis in R and Ward's method. The result showed the existence of 19.49 % of total area in high and very high priority category in two regions. The results of the Cluster Analysis showed that 35 % of the watersheds have homogeneous zones within the geometric and shape factor, while 65 % within the drainage and relief factor. In general, GIS, PCA and SWA methods are an efficient tool, which allows decision-making authorities for better planning and conservation of natural resources in hydrographic watersheds.

Amp-TB2 Protocol and Its Application to Amphiboles from Recent, Historical and Pre-Historical Eruptions of the Bezymianny Volcano, Kamchatka

This article reports a protocol on the application of Amp-TB2 (single-amphibole thermobarometry) based on detailed electron-microprobe analyses performed on homogeneous natural standards and synthetic glasses, and amphibole crystals (mostly phenocrysts) of volcanic products erupted by the Bezymianny volcano during its activity through time. The application of this protocol is facilitated by a new version of the model (Amp-TB2.1.xlsx) including an equation to identify heterogeneous domains (disequilibrium; not suitable for thermobarometric constraints) and homogenous (equilibrium) zones within amphibole crystals, which can be used to quantify the physicochemical parameters (i.e., pressure, P; temperature, T; volatile content in the melt, H2Omelt; oxygen fugacity, fO2) of “steady-state” magmatic crystallization. Application examples of the protocol, showing detailed core–rim microprobe data and physicochemical parameter variations in representative amphibole phenocrysts of the Bezymianny are also reported. The depth (and P) estimated by Amp-TB2.1 for this volcano are compared to seismic tomography results. Amp-TB2.1 results mainly show (1) that the Bezymianny is characterized by a very dynamic feeding system where the magma is stored at shallow crustal levels before recent activity periods characterized by climatic events and (2) that the pre-eruptive depth of magma storage generally increases with the age of the investigated products.

Identification of homogenous zones using riding speed time-series to reveal the spatial structure of the urban pace

Traffic speed is essential for the measurement of urban pace. The riding speed metric is one of the most effective ways to identify the underlying dynamic patterns and structures of urban pace. However, few studies have explored the patterns of urban pace reflected by riding speed differentiation of public shared bicycle groups. To this end, this study first infers the riding speeds under unknown path conditions based on the origin–destination of large sample riding records in Suzhou, China. Second, an identification model of homogenous zones for urban pace is established by neighboring station search and time-series similarity-based neighboring station merger. Finally, the fluctuation and deviation indexes of the fitted rhythm curves are adopted to measure the heterogeneity degree of the urban pace in each homogenous zone. Results show that the urban paces based on riding speed have significant spatial differentiation characteristics in the city, and the generated homogeneous zones show a certain stable structure in urban space. The old town has the fastest urban pace and is at a consistently high level, the new district has a faster urban pace and a double-peak pattern, the suburb area has a moderate urban pace and shows a single-peak pattern, while the peripheral area has the slowest urban pace with no significant pace spikes. Meanwhile, the waveform of the urban pace in different zones have a delay of approximately one hour. This study proposes an effective approach to reflect the spatiotemporal homogeneity and heterogeneity of urban pace through the dynamic traffic flow elements, which can be universally applied to other cities and is helpful for planning more dynamic urban functional areas and detecting life stress of urban residents.