Mixed Geographically Weighted Regression Research Articles

MIXED GEOGRAPHICALLY WEIGHTED REGRESSION (MGWR) WITH ADAPTIVE WEIGHTING FUNCTION IN POVERTY MODELING IN NTT PROVINCE

Poverty modeling is a crucial economic and social development issue in various regions, including in East Nusa Tenggara (NTT) Province. This research proposes using the Mixed Geographically Weighted Regression (MGWR) model with an adaptive Bisquare weighting function to analyze variables influencing poverty levels in NTT Province. The MGWR model is an extension of the Geographically Weighted Regression (GWR), which allows some variables in the model to have local effects while others have global effects. The adaptive weighting function in the MGWR model enhances the analysis by providing different weights at each location according to its local characteristics, thus making the results more accurate and representative for each area. The data includes economic, social, and infrastructure variables from 22 districts/cities in NTT Province for 2023. The MGWR model with an adaptive weighting function is applied to model the relationship between these variables and poverty levels. The analysis integrates statistical software to manage and analyze spatial data. The study findings show that the MGWR model with an adaptive weighting function offers better estimates than the global regression and GWR models. The results revealed the smallest AIC value for the MGWR model at 104.1888, compared to the global regression model at 140.1427 and the GWR model at 117.6174. This model successfully identifies significant local and global variables and shows variations in influence at different locations in NTT Province. These findings provide valuable insights for policymakers and practitioners in designing and implementing more effective poverty alleviation strategies tailored to local conditions in NTT Province.

Mapping urban heat: Unveiling nature-based solutions in Sari City, Iran.

Amidst growing concerns surrounding escalating temperatures and the rapid expansion of urban areas, researchers are delving into the intricate interplay between urban morphology, the intensifying Urban Heat Island (UHI) effect, and the efficacy of Nature-based Solutions (NbS). Despite sustained research efforts, a critical knowledge gap persists in precise modeling and predicting UHI mitigation through NbS interventions. This gap is primarily attributed to nuanced spatial variations at the neighborhood level, where these interventions are commonly implemented. This study addressed the challenge by employing a comprehensive grid structure consisting of 699 cells, covering the entirety of Sari City, Iran, as the spatial scale. The investigation identified five hot zones within the study area. To scrutinize the relationships between the UHI effect and various influencing factors of neighborhood morphology, diverse models such as Ordinary Least Squares (OLS), Random Forest Regression (RFR), Geographically Weighted Regression (GWR), and Mixed Geographically Weighted Regression (MGWR) were applied. Remarkably, the modeling fit for simulated data experienced a significant enhancement with the implementation of MGWR models. Five NbS scenarios were created and simulated using model outputs. The analysis revealed diverse responses across different segments. Notably, green roofs emerged as the most effective mid-term scenario for mitigating the UHI effect.

SPATIAL MODELING OF POVERTY IN BENGKULU PROVINCE WITH MIXED GEOGRAPHICALLY WEIGHTED REGRESSION

The percentage of poor people in Bengkulu Province is high from year to year. The poverty rate in Bengkulu Province also tends to fluctuate. If there is a decrease in the poverty rate, the decrease is relatively small. Poverty in the regions of Bengkulu Province also varies from district to district, subdistrict, and village to village, because poverty data is spatial data that varies regionally. The diversity of poverty data in Bengkulu Province is influenced by spatial effects, namely spatial dependency and spatial heterogeneity. Spatial dependency occurs due to spatial error correlation in cross section data, while spatial heterogeneity occurs due to random area effects, which is the difference between one region and another. Therefore, classical methods are not qualified enough to analyze the resulting diversity. This research will model the poverty of each district/city in Bengkulu Province using Mixed Geographically Weighted Regression (MGWR), because this method is quite complex in modeling data that contains spatial heterogeneity and variations in geospatial data. This modeling aims to identify and analyze poverty indicators in Bengkulu Province spatially, namely based on poverty data in each district/city in Bengkulu Province. The results showed that by using the MGWR method, the variables that locally influence the percentage of extreme poor people in each district/city in Bengkulu Province are Female Household Head Gender and not having a waterheater . Meanwhile, the variable that has a global effect on the percentage of the extreme poor in each district/city in Bengkulu Province is not having a flat screen television ().

Modeling the Flood Disaster in South Kalimantan Using Geographically Weighted Regression and Mixed Geographically Weighted Regression

The flood disaster in South Kalimantan is a crucial problem that needs to be addressed because the impact is relatively severe. So, this study aims to model flood disasters in South Kalimantan based on factors suspected to be the cause, including population density, rainfall, residential area, and forest area. This study uses two methods of spatial statistics, namely the Geographically Weighted Regression (GWR) and Mixed Geographically Weighted Regression (MGWR) methods. The weighting used is Adaptive Gaussian. The modeling results show that the GWR model is superior in explaining the causes of flood events in South Kalimantan, which is indicated by the highest coefficient of determination value of 95.62% compared to the regression and MGWR models. Nonetheless, the MGWR model can explain the causes of flooding in Kalimantan. The GWR and MGWR models show that the area that is vulnerable to flooding is Balangan District. The results of this study contribute to providing alternative information for disaster mitigation to minimize losses.

Pemodelan Mixed Geographically Weighted Regression-Spatial Autoregressive (MGWR-SAR) pada Kasus HIV di Indonesia

In general, spatial regression is used to model one of the spatial effects, namely spatial dependency or heterogeneity. For the effects of spatial dependencies, the models that have been used frequently follow Elhost's taxonomy, with the spatial dependencies being on the response, predictor, or error. Whereas for the effect of spatial heterogeneity generally use geographically weighted regression models (GWR) or if there are global predictors use mixed geographically weighted regression (MGWR). The data used in this study are cases of Human Immunodeficiency Virus (HIV) per 100,000 population as a response variable, and key populations, positive cases in pregnant women, tuberculosis patients, poverty rate, and unemployment rate as predictors. In the data used, there are spatial dependencies and heterogeneity. The MGWR-SAR is a model that can be used if the data has both spatial effects. This study aims to determine the factors influencing HIV cases in districts/cities in Indonesia using a spatial model. The results showed that the combined model of GWR and spatial autoregressive regression (SAR) was the best model. Key population explanatory variables have a global and significant influence. Other explanatory variables that have local influence are positive cases in pregnant women, tuberculosis patients, poverty rates, and unemployment rates.

Advancing and lagging effects of weather conditions on intercity traffic volume: A geographically weighted regression analysis in the Guangdong-Hong Kong-Macao Greater Bay Area

With the rapid expansion of urban areas, intercity highways have become crucial for daily transportation. Traffic administrators and planners increasingly rely on evaluating highway traffic volume. This paper aims to investigate the relationship between various factors and intercity traffic volume, with a specific focus on exploring the advancing and lagging effects of weather conditions on traffic volume in the districts of urban agglomerations. Using multiple data sources in the Guangdong-Hong Kong-Macao Greater Bay Area, including weather factors (i.e., rain, temperature, wind, and visibility), traffic factors (i.e., total traffic volume and travel time), and other factors (i.e., node degree, hub cities, and time of day), a mixed geographically weighted regression (MGWR) model is applied to examine the spatial heterogeneity of these factors. The results show that intercity traffic volume is influenced by weather, traffic, and other factors. Additionally, the advancing and lagging effects of different weather factors exhibit spatial heterogeneity across districts. Moreover, the weather lagging effect has a more significant impact than the advancing effect on intercity traffic volume. These findings provide valuable insights into the impact of weather on intercity travel volume and offer precise traffic guidance for intercity travelers.

Exploring associations between built environment and crash risk of children in school commuting

Understanding how built environment are associated with crash risk (CR) in school commuting is essential to improving travel safety through land use and transportation policies. Scholars often assume that this relationship is consistent across space, but this may lead to inconsistent estimates. To address this issue, using data in Shenzhen, China, the data covers traffic accident data of children taken from police incident reports and supplemented with local land use, transportation network and specific school information. The measurement model of crash scale was conducted to represent crash severity, and the CR was further quantified. The study applies three models, spatial dubin model (SDM), geographically weighted regression (GWR), and mixed GWR (MGWR), to explore spatio-temporal heterogeneity relationships between built environment attributes and CR of children in school commuting. The findings reveal that the crash scale can better represent crash severity of school commuting than a single indicator. Policy interventions should be targeted at specific spatial scales, school types, and time windows to effectively improve travel safety. However, there are some common findings. It is recommended to use a scale of 200 m to explain the relationship between the variables. The MGWR model outperforms the other two models. To reduce CR, it is important to consider lower road network density, a reasonable layout of educational facilities, fewer bus routes, and more on-street parking spaces. Our findings can help to enrich the understanding of associations between land use and CR of children, as well as offer local planning and operating guidance for creating child-friendly environment.

PEMODELAN MIXED GEOGRAPHICALLY WEIGHTED REGRESSION (MGWR) DENGAN JARAK EUCLIDEAN DAN JARAK MANHATTAN (STUDI KASUS : KEMATIAN BAYI NEONATAL DI JAWA TENGAH TAHUN 2018-2020)

Neonatal is a condition of babies from birth to 28 days. Data on Indonesia's health profile in 2020 showed that 72% of the number of deaths of toddlers occurred during the neonatal period and Central Java became the highest province of cases. Factors that are suspected to influence are the number of low birth weight babies (X1), the number of obstetric complications (X2), the number of Puskesmas (X3), the number of Posyandu (X4), the number of exclusive breastfeeding babies 0-6 months (X5), the number of pediatricians (X6), the number of ambulance cars (X7). Linear regression modeling on the number of neonatal infant deaths in Central Java has a heteroskedasticity problem so that Geographically Weighted Regression (GWR) is used. The distances used are Euclidean and Manhattan as well as the weighting function using Exponential and Tricube Kernel with Fixed Bandwidth. GWR modeling shows that not all independent variables are local, so Mixed Geographically Weighted Regression (MGWR) is used. The results of the GWR analysis with both distances and the two variable weighting functions are not local, including X2, X5, and X7. MGWR distance Manhattan Fixed Tricube Kernel became the better model, as the AICC value was smaller.

Spatial Distribution Characteristics and Influencing Factors of Soil Organic Carbon Density in Yellow River Basin Based on MGWR Model

As the largest terrestrial carbon pool, the spatial distribution characteristics and influencing factors of soil organic carbon have important implications for global carbon cycle processes. Soil organic carbon density (SOCD) and influencing factors were predicted in the Yellow River basin using a mixed geographically weighted regression (MGWR) model based on soil organic carbon density data and environmental factors. The results showed that:① the SOCD ranged from 0-14.82 kg·m-2 and 0-32.39 kg·m-2 for the soil depths of 0-20 cm and 0-100 cm, with mean values of 3.48 kg·m-2 and 8.07 kg·m-2 and reserves of 2.76 Pg and 6.48 Pg, respectively. The high SOCD value areas were mainly located in the southern part of the Qinghai-Tibet Plateau and Loess Plateau, and the low value areas were located in the eastern part of the upper Yellow River and the inland flow area. ②Among the ecosystem types, the SOCD of soil depth in 0-20 cm was in the descending order of:forest>water body and wetland>other>grassland>farmland>settlement>desert, with mean values of 4.52, 4.31, 3.84, 3.73, 2.89, 2.78, and 2.22 kg·m-2, respectively, and the SOCD of the 0-100 cm soil depth was in the descending order of:water bodies and wetlands>forest>other>grassland>farmland>settlement>desert, with mean values of 9.58, 9.58, 8.85, 8.66, 7.07, 6.81, and 5.29 kg·m-2, respectively. The SOCR in descending order was:grassland>farmland>forest>desert>water bodies and wetlands>settlement>others, with 1.40, 0.60, 0.47, 0.11, 0.07, 0.06, and 0.05 Pg at a soil depth of 0-20 cm and 3.31, 1.49, 0.99, 0.26, 0.17, 0.14, and 0.12 Pg at a soil depth of 0-100 cm, respectively. ③ The main factors affecting the SOCD distribution were intercept, profile curvature, NDVI, and precipitation; in addition, curvature and silt also had important effects on the deep SOCD distribution in the Yellow River basin. Among the ecosystem types, precipitation and NDVI were the main factors affecting the SOCD distribution. The intercept also had important effects on the SOCD distribution in the all ecosystems except forests, whereas curvature and silt only had important effects on deserts and other ecosystems. These results revealed the spatial distribution of SOCD, influencing factors, and SOCR in the Yellow River basin and can provide a scientific basis for carbon balance, soil quality evaluation, and ecological management restoration and consolidation in the region.

EXPLANATORY ANALYSES OF WORK TRIP GENERATION USING MIXED GEOGRAPHICALLY WEIGHTED REGRESSION (MGWR)

Abstract. In transportation planning, forecasts have commonly followed the sequential four-step model in which, trip generation (production and attraction) plays a critical role. Among the methods applied to model trip generation, regression with Gaussian distribution of errors are recognized as the most prevailing techniques to describe the relationships between production/attraction and explanatory variables by estimating the global, fixed coefficients. Considering that, trip generation is almost impressed by spatial factors which vary over the study area; the main objective of this research is to apply Mixed Geographically Weighted Regression (MGWR) on 253 traffic analysis zones (TAZs) in Mashhad, Iran, by applying travel demand data and relating factors in 2018 to investigate the spatial non-stationarity which are not revealed when global specifications are applied. The influence of certain explanatory variables on response variables may be global, whereas others are local, accordingly, MGWR performs better compared with geographically weighted regression. The results of Moran’s I as spatial autocorrelation index performing on residuals of global, mixed models proved the reliability of the proposed model over the traditional one. The spatial model indicated an improvement in model performance using goodness-of-fit criteria with the coefficient of determination varying from 0.84–0.95 compared with 0.76 and 0.6 in the conventional model. The results of such analysis can provide descriptive and predictive tools at the planning-level for decision-makers.

Combining APHRODITE Rain Gauges-Based Precipitation with Downscaled-TRMM Data to Translate High-Resolution Precipitation Estimates in the Indus Basin

Understanding the pixel-scale hydrology and the spatiotemporal distribution of regional precipitation requires high precision and high-resolution precipitation data. Satellite-based precipitation products have coarse spatial resolutions (~10 km–75 km), rendering them incapable of translating high-resolution precipitation variability induced by dynamic interactions between climatic forcing, ground cover, and altitude variations. This study investigates the performance of a downscaled-calibration procedure to generate fine-scale (1 km × 1 km) gridded precipitation estimates from the coarser resolution of TRMM data (~25 km) in the Indus Basin. The mixed geographically weighted regression (MGWR) and random forest (RF) models were utilized to spatially downscale the TRMM precipitation data using high-resolution (1 km × 1 km) explanatory variables. Downscaled precipitation estimates were combined with APHRODITE rain gauge-based data using the calibration procedure (geographical ratio analysis (GRA)). Results indicated that the MGWR model performed better on fit and accuracy than the RF model to predict the precipitation. Annual TRMM estimates after downscaling and calibration not only translate the spatial heterogeneity of precipitation but also improved the agreement with rain gauge observations with a reduction in RMSE and bias of ~88 mm/year and 27%, respectively. Significant improvement was also observed in monthly (and daily) precipitation estimates with a higher reduction in RMSE and bias of ~30 mm mm/month (0.92 mm/day) and 10.57% (3.93%), respectively, after downscaling and calibration procedures. In general, the higher reduction in bias values after downscaling and calibration procedures was noted across the downstream low elevation zones (e.g., zone 1 correspond to elevation changes from 0 to 500 m). The low performance of precipitation products across the elevation zone 3 (>1000 m) might be associated with the fact that satellite observations at high-altitude regions with glacier coverage are most likely subjected to higher uncertainties. The high-resolution grided precipitation data generated by the MGWR-based proposed framework can facilitate the characterization of distributed hydrology in the Indus Basin. The method may have strong adoptability in the other catchments of the world, with varying climates and topography conditions.

Low Welfare Status Modeling Using Mixed Geographically Weighted Regression Method with Fixed Tricube Weighting Function

Mixed Geographically Weighted Regression (MGWR) is a method for analyzing spatial data in regression that produces local and global parameters. Parameter estimation using WLS with a fixed tricube weighting function. The object of research in this study is poor population (X1), female household heads (X2), the education (X3), individuals with disabilities (X4), individuals having chronic disease (X5), individuals works (X6), uninhabitable houses (X7), and low welfare status (Y). This reseach applied to the low welfare status (Y) of each district/town in Central Java in 2019, and produced local variables are X1, X3, X5 and global variables are X2, X4, X6, and X7. However, only X1, X4, and X7 have a significant effect on Y in each district/town in Central Java, and X3 has a significant effect on only a few districts/cities, the other, X2, X5, and X6 have no significant effect on the model. The predictor variable has an effect of 98.92% on the model while the remaining 1.18% affected by other factors. The MGWR method divides 2 groups based on significant variables, (a) The first, a district/town whose low welfare status affected by X1, X3, X4, X7 covering Cilacap, Purbalingga, Kendal, Batang, Brebes, Pekalongan Town, and Tegal Town, (b) The second, districts/town whose low welfare status affected by X1, X4, X7 covering Banjarnegara, Purworejo, Temanggung, Kudus, Wonosobo, Pekalongan, Pemalang, Jepara, Wonogiri, Boyolali, Tegal, Magelang, Sukoharjo, Banyumas, Grobogan, Klaten, Karanganyar, Kebumen, Blora, Semarang Town, Pati, Sragen, Demak, Magelang Town, Salatiga Town, Surakarta Town, Semarang, and Rembang.

Combining downscaled-GRACE data with SWAT to improve the estimation of groundwater storage and depletion variations in the Irrigated Indus Basin (IIB)

The growth of agricultural production systems is a major driver of groundwater depletion worldwide. Balancing groundwater supply and food production requires localized understanding of groundwater storage and depletion variations in response to diverse cropping systems and surface water availability for irrigation. While advances through Gravity Recovery and Climate Experiment (GRACE) have facilitated estimating the groundwater storage (GWS) changes in recent years, the coarse resolution of GRACE data hinders the characterization of GWS variation hotspots. Herein, we present a novel spatial water balance approach to improve the distributed estimation of groundwater storage and depletion changes at a spatial scale that can detect the hotspots of GWS variation. We used a mixed geographically weighted regression (MGWR) model to downscale GRACE Level-3 data from coarse resolution (1° × 1°) to fine scale (1 km × 1 km) based on high resolution environmental variables. We then combined the downscaled GRACE-based GWS variations with results from a calibrated Soil and Water Assessment Tool (SWAT) model. We demonstrate an application of the approach in the Irrigated Indus Basin (IIB). Between 2002 and 2019, total loss of groundwater reserves varied in the IIB's 55 canal command areas with the highest loss observed in Dehli Doab by >50 km3 followed by 7.8–49 km3 in the upstream, and 0.77–7.77 km3 in the downstream canal command areas. GWS declined by −325.55 mm/year at Dehli Doab, followed by −186.86 mm/year at BIST Doab, −119.20 mm/year at BARI Doab, and −100.82 mm/year at JECH Doab. The rate of groundwater depletion is increasing in the canal command areas of Delhi Doab and BIST Doab by 0.21–0.35 m/year. Larger groundwater depletion in some canal command areas (e.g., RACHNA, BIST Doab, and Delhi Doab) is associated with the rice-wheat cropping system, low rainfall, and low flows from tributaries.

Spatial non-stationary characteristics between grass yield and its influencing factors in the Ningxia temperate grasslands based on a mixed geographically weighted regression model

Spatial models are effective in obtaining local details on grassland biomass, and their accuracy has important practical significance for the stable management of grasses and livestock. To this end, the present study utilized measured quadrat data of grass yield across different regions in the main growing season of temperate grasslands in Ningxia of China (August 2020), combined with hydrometeorology, elevation, net primary productivity (NPP), and other auxiliary data over the same period. Accordingly, non-stationary characteristics of the spatial scale, and the effects of influencing factors on grass yield were analyzed using a mixed geographically weighted regression (MGWR) model. The results showed that the model was suitable for correlation analysis. The spatial scale of ratio resident-area index (PRI) was the largest, followed by the digital elevation model, NPP, distance from gully, distance from river, average July rainfall, and daily temperature range; whereas the spatial scales of night light, distance from roads, and relative humidity (RH) were the most limited. All influencing factors maintained positive and negative effects on grass yield, save for the strictly negative effect of RH. The regression results revealed a multiscale differential spatial response regularity of different influencing factors on grass yield. Regression parameters revealed that the results of Ordinary least squares (OLS) (Adjusted R2 = 0.642) and geographically weighted regression (GWR) (Adjusted R2 = 0.797) models were worse than those of MGWR (Adjusted R2 = 0.889) models. Based on the results of the RMSE and radius index, the simulation effect also was MGWR > GWR > OLS models. Ultimately, the MGWR model held the strongest prediction performance (R2 = 0.8306). Spatially, the grass yield was high in the south and west, and low in the north and east of the study area. The results of this study provide a new technical support for rapid and accurate estimation of grassland yield to dynamically adjust grazing decision in the semi-arid loess hilly region.

PEMODELAN MIXED GEOGRAPHICALLY WEIGHTED REGRESSION DENGAN ADAPTIVE BANDWIDTH UNTUK ANGKA HARAPAN HIDUP (Studi Kasus : Angka Harapan Hidup di Jawa Tengah)

Life expectancy at birth (AHH) is an estimate of the years a person will take from birth. AHH is used as an indicator of public health and welfare. These two indicators are of concern to the government in relation to human development. It is hoped that the AHH value will continue to increase so that the quality of human development will also increase. Modeling of the factors that influence AHH needs to be done so that efforts to increase AHH become more effective.The AHH value for Central Java (Central Java) in 2020 is 74.37. Factors thought to influence AHH in Central Java are the percentage of poor people (X1), the percentage of households with proper sanitation (X2), the percentage of children under five who are fully immunized (X3) and the open unemployment rate (X4). The assumption of homoscedasticity in AHH modeling in Central Java using linear regression was not fulfilled, meaning that there was spatial heterogeneity between districts/cities, so the Geographically Weighted Regression (GWR) method was used. The weighting function used is the Bisquare and Tricube kernels with adaptive bandwidth. The GWR method will encounter problems if not all independent variables are local, so the Mixed Geographically Weighted Regression (MGWR) method is used. The results of the GWR analysis for the two weighting functions are that the X1 variable is not local, so the MGWR method is used. The results of MGWR modeling for the two weighting functions are that local variables and global variables have a significant effect. The best model is the MGWR model with Kernel Tricube weighting because it has the smallest AICc value. Keyword : AHH, GWR, MGWR, Adaptive Kernel Bisquare, Adaptive Kernel Tricube, AICc

Spatially Varying Relation between Built Environment and Station-Level Subway Passenger-Distance

As a sustainable mode of transportation, subways bring great convenience to the society. Although there have been many studies examining the relationship between the built environment and the station-level ridership, those studies focused mainly on the ridership, which is defined as the number of trips for each station. While ridership is an important indicator for evaluating subway demand, passenger-distance is another critical indicator that incorporates distance into demand evaluation, which has not yet been fully explored. To fill this gap, this paper investigates the relationship between the built environment around stations and the station-level passenger-distance (SLPD). As noted in previous studies, the relationship between the built environment and travel demand can vary by space. Therefore, a geographically weighted regression (GWR) model and a mixed geographically weighted regression (MGWR) model have been used to explore this spatially varying relationship using Chengdu, China, as an example case. The results were compared with that of an ordinary least squares (OLS) model. The comparison shows that the MGWR model that considers both global and local variables has the best goodness of fit. Results also show that 11 of the 25 potential variables are significantly related to SLPD. The accessibility of the station, station type, such as transfer or terminal, number of bus stops, number of restaurants, density of building area, density of the national road network, and density of the provincial road network, all have a positive correlation with SLPD. Meanwhile, the variables, whether it is a newly opened subway station, density of living points of interest (POIs), and density of railroad network, are all negatively correlated with SLPD. Ten of the eleven significant variables (except accessibility) have spatially varying relationships with SLPD. These findings can serve a useful reference for transportation planners for the demand evaluation.

A Route Map for Successful Applications of Geographically Weighted Regression

Geographically Weighted Regression (GWR) is increasingly used in spatial analyses of social and environmental data. It allows spatial heterogeneities in processes and relationships to be investigated through a series of local regression models rather than a single global one. Standard GWR assumes that relationships between the response and predictor variables operate at the same spatial scale, which is frequently not the case. To address this, several GWR variants have been proposed. This paper describes a route map to decide whether to use a GWR model or not, and if so which of three core variants to apply: a standard GWR, a mixed GWR or a multiscale GWR (MS‐GWR). The route map comprises 3 primary steps that should always be undertaken: (1) a basic linear regression, (2) a MS‐GWR, and (3) investigations of the results of these in order to decide whether to use a GWR approach, and if so for determining the appropriate GWR variant. The paper also highlights the importance of investigating a number of secondary issues at global and local scales including collinearity, the influence of outliers, and dependent error terms. Code and data for the case study used to illustrate the route map are provided.

Uncovering the spatially heterogeneous effects of shared mobility on public transit and taxi

A Mixed Geographically Weighted Regression (GWR) model is applied to explore the effects of shared mobility trips on taxi and public transit ridership at the macro-level. Several essential variables, including socioeconomic, transportation, network, and land use data, are set as the causal factors. The experiment is conducted using the smart card data, vehicle GPS trajectories, and vehicle order data collected in Shenzhen City, China. We show that the Mixed GWR outperforms the basic GWR in model fitting and capturing the unobserved heterogeneity. The spatial analysis reveals that bike-sharing addresses the “last-mile” and “first-mile” problems to bus and metro in the urban periphery. It substitutes the bus and taxis in short-distance journeys in the city center. However, the over-placement of bike-sharing in some regions limits the flexibility of bike-sharing connections to the metro. In the city center, ride-hailing fills the gaps in bus coverage and competes with the metro. In the peripheral areas, ride-hailing replaces buses and improves the accessibility to metro stations. The transportation policy increases the cooperation between ride-hailing and taxis citywide, although competitions in few regions need to be solved. The abovementioned results provide policy suggestions to optimize the allocation of local transportation resources.

Reconstructing high-resolution gridded precipitation data using an improved downscaling approach over the high altitude mountain regions of Upper Indus Basin (UIB)

Understanding the basin-scale hydrology and the spatiotemporal distribution of regional precipitation requires high precision, as well as high-resolution precipitation data. We have made an attempt to develop an Integrated Downscaling and Calibration (IDAC) framework to generate high-resolution (1 km × 1 km) gridded precipitation data. Traditionally, GWR (Geographical weighted regression) model has widely been applied to generate high-resolution precipitation data for regional scales. The GWR model generally assumes a spatially varied relationships between precipitation and its associated environmental variables, however, the relationships need to remain constant (fixed) for some variables over space. In this study, a Mixed Geographically Weighted Regression (MGWR) model, capable of dealing with the fixed and spatially varied environmental variables, is proposed to downscale the Original-TRMM precipitation data from a coarse resolution (0.25o × 0.25o) to a high-resolution (1 km × 1 km) for the period of 2000–2018 over the Upper Indus Basin (UIB). Additionally, accuracy of the downscaled precipitation data was further improved by merging it with the recorded data from rain gauge stations (RGS) using two calibration approaches such as Geographical Ratio Analysis (GRA) and Geographical Difference Analysis (GDA). We found MGWR to perform better given its higher R2 and lower RMSE and bias values (R2 = 0.96; RMSE = 56.01 mm, bias = 0.014) in comparison to the GWR model (R2 = 0.95; RMSE = 60.76 mm, bias = 0.094). It was observed that the GDA and GRA calibrated-downscaled precipitation datasets were superior to the Original-TRMM, yet GRA outperformed GDA. Annual precipitation from downscaled and calibrated-downscaled datasets was further temporally downscaled to obtain high-resolution monthly and daily precipitations. The results revealed that the monthly-downscaled precipitation (R2 = 0.82, bias = −0.02 and RMSE = 11.93 mm/month) and the calibrated-downscaled (R2 = 0.89, bias = −0.006 and RMSE = 9.19 mm/month) series outperformed the Original-TRMM (R2 = 0.72, bias = 0.14 and RMSE = 19.8 mm/month) as compared to the RGS observations. The results of daily calibrated-downscaled precipitation (R2 = 0.79, bias = 0.001 and RMSE = 1.7 mm/day) were better than the Original-TRMM (R2 = 0.64, bias = − 0.12 and RMSE = 6.82 mm/day). In general, the proposed IDAC approach is suitable for retrieving high spatial resolution gridded data for annual, monthly, and daily time scales over the UIB with varying climate and complex topography.

Characteristics of parameter mixed geographically weighted regression model : global (a-group) and local (b-group)

Mixed Geographically Weighted Regression (MGWR) is a combination of a linear regression model and a Geographically Weighted Regression (GWR) model that considers several variables to be constant and some to vary spatially. The MGWR model also considers location characteristics as indicated by global and local variables. The MGWR model is more flexible than the GWR model because its characteristics are the same for each location. These characteristics are shown in the variables contained in the MGWR model, namely global and local variables. Global variables produce global a-group parameters that have the same effect for all observation locations and local variables produce local b-group parameters that have different effects for each observation location. This article reviews the characteristics of global (a-group) and local (b-group) parameters in the MGWR model. The research shows that the two parameters can be predicted by different methods, estimating global parameters in the regression model by the least squared (LS) method and estimating local parameters by the weighted least squared method (WLS). The difference in this parameter estimation method is due to local parameters influenced by the presence of observational characteristics so that the assumption does not occur heteroscedasticity cannot be obtained.