E-commerce and online shopping have become more convenient due to the rapid growth of the internet logistic industry in many developed countries and are particularly popular and suitable in China. The method is primarily based on logistic points like attended collection and delivery points (ACDPs), an emerging industry for economic development. This article includes descriptive statistics, and spatial analysis to analyse the location distribution, and influencing factors of ACDPs in Nanjing City using point of interest data (POI) of Cainiao stations and China Post stations. The results show that the spatial distribution of ACDPs in Nanjing is asymmetrical, displaying a trend to the northwest direction and a difference in the trend between the S-W and N-E axes. Their layout creates four main core areas and the number of sites decreases with distance from the core. Most of the ACDPs are in urban areas and on residential and industrial land. This study provides insightful ideas for decision-makers and planners to help formulate policies that can lead to more sustainable logistic enterprises development and for the companies that want to establish successful CDP networks in big cities.

High-resolution soil moisture (SM) information is essential for regional to global hydrological and agricultural applications. The Soil Moisture Active Passive (SMAP) offers daily global composites of SM at coarse-resolution 9 and 36 km, with data gaps limiting its local application to depict SM distribution in detail. To overcome the aforementioned problem, a downscaling and gap-filling novel approach was adopted, using random forest (RF) and artificial neural network (ANN) algorithms to downscale SMAP SM data, using land-surface variables from moderate-resolution imaging spectroradiometer (MODIS) onboard Aqua and Terra satellites from the years 2018 to 2019. Firstly, four combinations (RF+Aqua, RF+Terra, ANN+Aqua, and ANN+Terra) were developed. Each combination downscaled SMAP SM at a high resolution (1 km). These combinations were evaluated by using error matrices and in situ SM at different scales in the ShanDian River (SDR) Basin. The combination RF+Terra showed a better performance, with a low averaged unbiased root mean square error (ubRMSE) of 0.034 m3/m3 and high averaged correlation (R) of 0.54 against the small-, medium-, and large-scale in situ SM. Secondly, the impact of various land covers was examined by using downscaled SMAP and in situ SM. Vegetation attenuation makes woodland more error-prone and less correlated than grassland and farmland. Finally, the RF+Terra and ANN+Terra combinations were selected for their higher accuracy in gap filling of downscaled SMAP SM. The gap-filled downscaled SMAP SM results were compared spatially with China Land Data Assimilation System (CLDAS) SM and in situ SM. The RF+Terra combination outcomes were more humid than ANN+Terra combination results in the SDR basin. Overall, the RF+Terra combination gap-filled data showed high R (0.40) and less ubRMSE (0.064 m3/m3) against in situ SM, which was close to CLDAS SM. This study showed that the proposed RF- and ANN-based downscaling methods have a potential to improve the spatial resolution and gap-filling of SMAP SM at a high resolution (1 km).

Water is a primary element for human life on Earth. Fresh water, which includes rivers, lakes, streams, and ponds, contributes less than one thousandth of a percent of the total water on Earth, but it is critical for the environment and human life. Change in land use and land cover (LULC) is a foremost concern in global environment change. Rapid changes in LULC lead to the degradation of ecosystems and have adverse effects on the environment. There is an urgent need to monitor changes in LULC and evaluate the effects of these changes in order to inform decision makers on how to support sustainable development. This study used Moderate Resolution Imaging Spectroradiometry images to detect and investigate changes in LULC patterns in Gilgit-Baltistan, Pakistan, between 2008 and 2017. Six types of LULC were used to explain the major changes of LULC in the study area. The results showed that there was a reduction of barren lands and an increase of urban areas. It also showed an inconsistent behavior of water bodies during the study. Snow area, which also increased, needs further investigation.

In the context of rapid urbanization, Urban Heat Island (UHI) is considered as a major anthropogenic alteration in Earth environments, and its temporal trends and future forecasts for large areas did not receive much attention. Using land surface temperature (LST) data from MODIS (Moderate Resolution Imaging Spectro-radiometer) for years 2006 to 2020, we quantified the temporal trends of daytime and nighttime surface UHI intensity (SUHII, difference of urban temperature to rural temperature) using the Mann-Kendall (MK) trend test in six major cities of the Punjab province of Pakistan and estimated the future SUHII for the year 2030 using the ARIMA model. Results from the study revealed that the average mean SUHII for daytime was noted as 2.221 °C and the average mean nighttime SUHII was noted as 2.82 °C for the years 2006 to 2020. The average mean SUHII for daytime and nighttime exhibited increasing trends for all seasons and annually, and for the daytime spring season it showed a maximum upward trend of 0.486 °C/year (p < 0.05) and for the nighttime annual SUHII with an increasing rate of 0.485 °C/year (p < 0.05) which exhibited a maximum upward trend. The ARIMA model forecast suggested an increase of 0.04 °C in the average daytime SUHII and an increase of 0.1 °C in the average nighttime SUHII until 2030. The results from this study highlight the increasing trends of daytime and nighttime SUHII, ARIMA also forecasted an increase in daytime and nighttime SUHII, suggesting various strategies are needed for an effective mitigation of the UHI effect.

The sustainable development of collection and delivery points and urban street network is an important consideration of logistic planners. Urban street networks have a significant impact on collection and delivery points’ location, but the spatial relationship between the centrality of urban street network and collection and delivery points has not been studied using spatial design network analysis. In a multiple centrality assessment model, we used point of interest and street network data to evaluate the location of two types of collection and delivery points and the centrality of streets in Nanjing city, based on four indicators: closeness, betweenness, severance, and efficiency. Then, kernel density estimation and spatial autocorrelation are used to study spatial patterns of distribution and centrality coupling effects of urban street network and collection and delivery points. The results show that the centrality of Nanjing streets has a big influence on the location of the collection and delivery points, and the directions of different types of centrality also vary. The location of the Cainiao Stations are largely related to closeness, followed by betweenness, severance, and efficiency. China Post Stations and street centrality have a weak correlation between efficiency and severance, but no correlation between closeness and betweenness. Our results can help logistics enterprises and urban planners to develop collection and delivery points’ network based on the urban street network.