Spatial Impact Research Articles

Spatial impact on inflation of Java Island prediction using Autoregressive Integrated Moving Average (ARIMA) and Generalized Space-Time ARIMA (GSTARIMA)

Inflation is one of macroeconomic issues in Indonesia that needs to be controlled. Inflation could happen because of widespread increases in the cost of goods and services. Annual inflation rate in Indonesia on 2008 to 2023 are quite fluctuating and several periods are not achieved inflation target yet. One of the ways to control inflation is by making predictions for the upcoming period. Java Island is the biggest contributor on economy and Gross Domestic Product (GDP) in Indonesia so it can be considered as general indicator to measure overall inflation rate of Indonesia. Thus, data used in this study is monthly inflation at each province in Java Island from January 2008 to December 2023. This study using two methods, Autoregressive Integrated Moving Average (ARIMA) for univariate time series prediction and Generalized Space-Time ARIMA (GSTARIMA) for multivariate time series prediction with a spatial factor. The results of both models will be compared to determine which model has better accuracy. Based on RMSE value, GSTARIMA model has least average RMSE value, which is 0.113 compared with ARIMA model which has average RMSE value 0.319 thus it can conclude that spatial factors addition could increase accuracy on inflation prediction in Java Island.•This paper purposes to get Java Island's inflation rate prediction to determine better policy on controlling cost of goods and services.•Best model using GSTARIMA methods is GSTARMA(1,1) with distance invese matrix that indicate that coordinate point of each location increase performance of inflation rate prediction.•The result indicate GSTARIMA has better accuracy than ARIMA for inflation prediction in Java Island based on RMSE value.

Heterogenization of microplastic communities in lakes of the Qinghai-Tibetan Plateau driven by tourism and transport activities

The Qinghai-Tibetan Plateau has a booming tourism industry and an increasingly sophisticated road system. There is a paucity of studies quantifying the contributions of anthropogenic and natural factors to microplastic pollution in remote plateau areas. In this study, water and sediment samples were collected from eight lake tourist attractions and four remote lakes in northern and southern regions of the Qinghai-Tibetan Plateau. Microplastics were detected in all samples, with a mean abundance of 0.78 items/L in water and 44.98 items/kg in sediment. The abundance of microplastics in the study area was lower than previously observed in more populated areas of China. Small-sized (<1 mm and 1–2 mm), fiber, and transparent microplastics were predominant, with polyethylene and polypropylene microplastics as the primary polymer types. The compositions of microplastic communities indicated that tourism and road networks were the major sources of microplastics in the lakes. Distance-decay models revealed greater influence of environmental distances on microplastic community similarity than geographic distance. Compared to climate factors, urban spatial impact intensity and traffic flow impact played a leading role in the structuring of microplastic communities in lake water and sediment. Our findings provide novel quantitative insights into the role of various factors in shaping the distribution patterns of microplastic communities in plateau lakes.

Can industrial intelligence promote carbon emission efficiency? --empirical research based on the Yangtze River Economic Belt

The rapid development of intelligent technology characterized by robots under the fourth scientific and technological revolution provides a favorable opportunity for the accurate decision-making of urban pollution control and the effective achievement of the goal of carbon emission reduction in China. This research uses the robot penetration rate as a representative and characteristic index of industrial intelligence development, based on panel data from 108 cities in the Yangtze River Economic Belt (YEB) from 2006 to 2020. It then uses panel quantile regression, spatial measurement, and threshold effect models to provide a more thorough theoretical and empirical discussion of the impact, mechanism, and spatial effect of industrial intelligence development on urban carbon emission efficiency (CEE). Urban CEE may be greatly increased with the use of industrial intelligence, and this finding remains true even after endogeneity and robustness tests are controlled; From an action mechanism perspective, industrial intelligence advances technology, optimizes industrial structure, and ultimately enhances regional CEE; There is a Matthew effect on the degree of development of carbon emission efficiency, and the impact of industrial intelligence on CEE is more pronounced in non-resource-based cities and the lower portions of YEB; Urban CEE increases positively with the spatial spillover impact of industrial intelligence development. The ability for regional sustainable development will be significantly increased if cross-regional cooperative prevention and control of environmental governance can be successfully achieved. This study verifies the enabling effect of industrial intelligence development on the improvement of urban CEE, and provides enlightenment for China to improve industrial intelligence development strategies and policies to achieve regional high-quality development.

Optimizing Urban Green Spaces for Air Quality Improvement: A Multiscale Land Use/Land Cover Synergy Practical Framework in Wuhan, China

Air pollution, particularly fine particulate matter (PM2.5), poses a significant health risk, especially in high-density urban areas. Urban green space (UGS) can effectively mitigate this pollution. Despite their potential, strategies for effectively leveraging Land Use/Land Cover (LULC) optimization to combat PM2.5 remain largely unexplored. Ordinary least squares (OLS), geographically weighted regression (GWR) and multiscale geographically weighted regression (MGWR) were employed to investigate the spatial heterogeneity relationship between UGS conversion and PM2.5 fluctuations across various scales and evolutionary stages, developing a multiscale practical framework for LULC synergy in combating air pollution. The areas of UGSs to/from other LULCs, PM2.5 concentrations and corresponding variation zones exhibited significant spatial clustering. These UGS conversions explained more than 65% of the PM2.5 changes in the study area, peaking at 76.4% explanatory power in the fourth stage. Compared to global spatial analysis (OLS: 0–0.48), local spatial regression analysis significantly improved the R2 value (GWR: 0.32–0.75, MGWR: 0.48–0.90), but the fitting quality of local spatial regression analysis decreased with increasing scale, highlighting the importance of scale diagnosis. A 2 km scale was identified as optimal for assessing the spatial heterogeneity impact of UGS and other LULC conversions on PM2.5 changes. Conversion areas from water bodies and bare land to UGSs maintain stable local spatial properties at this scale (bandwidths: 44–99). Our research provides new insights into LULC management and planning, offering a coordinated approach to mitigating urban air pollution. Additionally, a practical framework was established for addressing spatially continuous variables such as PM2.5, revealing effective approaches for addressing urban environmental issues.

Comparative Analysis of Spatial Impact of 2012 and 2022 Flood Disaster along Lower Niger Basin in Nigeria

Comparative Analysis of Spatial Impact of 2012 and 2022 Flood Disaster along Lower Niger Basin in Nigeria

"Beyond the Industrial Enclave: Analysing the Spatial and Socio-Economic Impact of Pithampur Automobile Cluster, India "

Industries are considered engines of growth for an economy. Developing countries like India have attempted to boost economic growth by initiating industrialization in areas unsuitable for industrial growth. Most studies on industries/industrial clusters focus on their financial performance, backward and forward linkages, and managing the supply and demand chains, rather than the socioeconomic and spatial influence on their locations and surrounding regions. Such studies are critical in determining which industry contributes to regional growth and what their spatial influences are. This paper contributes to this domain of research. In this study, the Pithampur Automobile Cluster (PAC) is examined. It was developed by the Government in 1984 with the intent to develop a backward area in the state of Madhya Pradesh in India. The study aims to discern PAC’s influence on its surroundings, encompassing Pithampur town, adjacent villages, and towns within a 12–15-kilometer radius. Data from multiple census years (1981, 1991, 2001, 2011) and Land Use Land Cover (LULC) Analysis through GIS for the years 1981, 1991, 2001, 2011, and 2021 are employed. Contrary to expectations, the study reveals that the impact of industrial development is primarily confined to the boundaries of Pithampur, with surrounding villages showing relative detachment. The emergence of growth corridors is attributed to the appeal of neighbouring towns that offer better amenities and a more economically viable living environment.

A ‘just transition’ for workers in the automotive sector? Survey evidence from the West Midlands

ABSTRACT Employment in the automotive sector is typically spatially concentrated and hence the impact of the transition to low-carbon technologies will have profound subnational effects. Although there is a rich literature around the spatial impact of automotive plant closures, the novelty of this paper lies in its focus on the impact on workers throughout the supply chain in the automotive sector. As such, this paper reports on lessons for supplier firms and workers in the West Midlands, in facilitating a ‘Just Transition’ in the automotive sector. The primary data collection consisted of a workforce survey in the UK of members of the Unite union (automotive section). Policy implications are explored with suggestions for further research on skills needs and the development of training provisions, to be conducted on a collaborative basis.

Urban dynamics and their implication on greenness trends: A 25-year retrospective study of the changing face of tokyo metropolitan area

Global metropolises and their needs for green space are rising due to demographic urbanization. Some phenomena contradicted to prevailing understanding have been observed in metropolises, such as the “sprawl shrinkage” associated with the increasing built-up area despite declining populations, and the positive relationship between population and greenness. To study these storylines, we conceptualized and mapped 5 urban dynamics by cross-referencing the“increase, stable, and decrease”of built-up area and population density based on threshold values of an introduced Normalized Difference Index (NDI). Furthermore, Kruskal-Wallis H Test was used to analyze the influence of different urban dynamic patterns on 3 greenness trends (greening, browning, and stable greenspace). From 1995 to 2021, Tokyo Metropolitan Area (TMA) gradually transited from intense urban expansion to a stabilization of built-up area and population density in the city center, declining urbanization in the peri-urban areas, and “sprawl shrinkage” in the far-outskirts. Moreover, compared to other urban dynamics, the pattern of built-up areas increases with a population density reduction of over 5% were conducive to maintaining NDVI values of 0.75 or higher. In the future, the ongoing demographic urbanization is highly likely to further exacerbate the phenomenon of urban-suburban disparity and intra-suburban differentiation. We propose that Asian metropolises should enhance regional cooperation and public-private partnerships to address potential regional ecological imbalances. Additionally, it is necessary to maximize the greenness within the given urban dynamics through figuring out the spatial impact mechanisms of urban development on greenness change from a long-term perspective.

Monitoring and enhancing spontaneous sustainability—a framework

The paper presents and addresses the concept of “spontaneous sustainability”, the informal and unplanned sustainable development of communities, as the basis and framework to support the notion, drives, and agendas of “formal sustainability”. It follows the interdependence between the “spontaneous sustainability” of “distinct” communities and their “cultural” characteristics, including history, spatial continuity, values and behaviors, the associated physical settings, and tangible and intangible products, which enabled them to continue, survive, and develop. The research aims at formulating and validating a “framework” for monitoring and enhancing the “spontaneous sustainability” of “distinct” communities to enable, support and link it, to the broader frameworks of “sustainable development”. The proposed “framework” combines “sustainability” dimensions: environmental, economic, and socio-cultural; and the levels of community contexts, and spatial impact. The “framework” is formulated by integrating selected “urban sustainability” approaches, and systems, with later research that developed the key aspects, criteria, and indicators of those systems, together with research addressing “social and cultural sustainability”, and assessment aspects, criteria, and indicators. The “framework” is developed and validated through a limited questionnaire, involving local experts and specialists, academics, and practitioners, to point out limitations and potential, and to suggest the relative importance of its components, key aspects, and criteria. The validation supported the research propositions, the formulation, and development of the proposed “spontaneous sustainability framework” and emphasized its flexibility to include other approaches and agendas, and potential for further development and application in distinct local communities.

Widespread impacts to precipitation of the East Palestine Ohio train accident

On 3 February 2023, a Norfolk Southern train derailment occurred in East Palestine, Ohio. The accident and subsequent fire resulted in the emissions of large amounts of hazardous compounds to the ambient atmosphere over many days. We used precipitation chemistry measurements routinely collected by the National Atmospheric Deposition Program (NADP) to estimate the spatial extent and chemical compounds deposited as a result of the accident. Our measurements revealed a large areal impact from the Midwest through the Northeast and likely Canada, and perhaps as far south as North Carolina (portions of 16 states, 1.4 million km2). Observations showed the expected high chloride concentrations, but also unexpectedly high pH (basic) and exceptionally elevated levels of base cations exceeding 99th percentiles versus the historic record. These results were consistent with the meteorological conditions and atmospheric trajectories, and were not due to highly-concentrated low volume precipitation samples or wildfires. The robust measurements of the NADP network clearly show that the impacts of the fire were larger in scale and scope than the initial predictions, and likely due to the uplift from the fire itself entraining pollutants into the atmosphere. A more detailed evaluation of the accident and resulting fire could further refine the full impact of the atmospheric concentrations, dry and wet deposition, and the more specific extent of the spatial impact.

UAV data and deep learning: efficient tools to map ant mounds and their ecological impact

AbstractHigh‐resolution unoccupied aerial vehicle (UAVs) data have alleviated the mismatch between the scale of ecological processes and the scale of remotely sensed data, while machine learning and deep learning methods allow new avenues for quantification in ecology. Ant nests play key roles in ecosystem functioning, yet their distribution and effects on entire landscapes remain poorly understood, in part because they and their mounds are too small for satellite remote sensing. This research maps the distribution and impact of ant mounds in a 20 ha treeline ecotone. We evaluate the detectability from UAV imagery using a deep learning model for object detection and different combinations of RGB, thermal and multispectral sensor data. We were able to detect ant mounds in all imagery using manual detection and deep learning. However, the highest precision rates were achieved by deep learning using RGB data which has the highest spatial resolution (1.9 cm) at comparable UAV flight height. While multispectral data were outperformed for detection, it allows for novel insights into the ecology of ants and their spatial impact on vegetation productivity using the normalized difference vegetation index. Scaling up, this suggests that ant mounds quantifiably impact vegetation productivity for up to 4% of our study area and up to 8% of the Betula nana vegetation communities, the vegetation type with the highest abundance of ant mounds. Therefore, they could have an overlooked role in nutrient‐limited tundra vegetation, and on the shrubification of this habitat. Further, we show the powerful combination UAV multi‐sensor data and deep learning for efficient ecological tracking and monitoring of mound‐building ants and their spatial impact.

How do the 3D urban morphological characteristics spatiotemporally affect the urban thermal environment? A case study of San Antonio

Urban landscapes, characterized by intricate interactions within a three-dimensional (3D) framework, give rise to complex mechanisms that underlie the Surface Urban Heat Island phenomenon. While global case studies have emphasized the impact of 3D urban morphological components on urban thermal environments, conducting quantitative assessments to identify spatial and seasonal variations in these relationships poses significant challenges. Based on Lidar and land cover dataset, this study first investigated the 3D urban morphology characteristics across nine components, and then assessed their spatial and seasonal impact on the urban thermal environment through Geographically Weighted Regression (GWR). We found that Impervious Surface Fraction (ISF) has the greatest warming effect, whereas the Vegetation Coverage Fraction (VCF) exerts the strongest cooling influence on the thermal environment across all seasons. Building Height (BH) and Street Aspect Ratio (SAR) exhibit spatially varying cooling effects, while Building Number (BN) and Standard Deviation of Building Height (BH_SD) have minimal warming impacts. Additionally, the influence of BH on the thermal environment is significant primarily in areas characterized by higher building coverage. VCF has a stronger cooling effect in central regions with limited vegetation during spring and winter, but this effect diminishes compared to suburban ones in summer and autumn. These findings provide valuable insights into developing effective heat mitigation strategies at both city and community levels, ultimately fostering sustainable urban development.

Convergence Analysis and Testing of Regional Effects of Green Finance on The Growth of Renewable Energy

ABSTRACT A paradigm shift in energy production and use is essential in the pursuit of carbon peaking and neutrality, and the expansion of renewable energy is a cornerstone of such a shift. This article first proposes an index that comprehensively measures renewable energy development in 30 provinces in China from 2010 to 2022. Then, the σ and β convergence models are applied to investigate the uniformity of renewable energy development. Furthermore, a spatial Durbin model is estimated to examine the spatial impact of green finance on renewable energy development. The results show that from 2010 to 2020, China has witnessed a significant increase in renewable energy use in its eastern, central and western regions, with the western region accelerating faster than its counterparts. During the same period, a remarkable trend of high convergence in renewable energy development has been observed, despite a slowdown in the spatial convergence rates between the central, eastern, western and national levels. The positive spatial spillover effect of renewable energy has increased significantly. The growth of renewable energy in the region and its surrounding areas is positively influenced by the growth of green finance.

How heterogeneous industrial agglomeration impacts energy efficiency subject to technological innovation:Evidence from the spatial threshold model

The relationship between industrial agglomeration (IA) and energy efficiency (EE) is significant for China to promote high-quality urban economic development and achieve China's dual carbon goals. Since technological innovation (TI) and green TI (GTI) are vital elements in the evolution of socioeconomic change and green development, this study employs a spatial threshold model to explore the technology innovation dependency of the influence of heterogeneous IA on EE based on prefecture-level city panel data of the manufacturing sector from 2006 to 2014 in China. This study finds that diversified IA (DIA) has a spatial threshold impact on EE subject to TI or GTI, while specialized IA (SIA) does not. DIA has significant positive direct, spillover, and overall effects on EE at the high TI and GTI thresholds. The distance attenuation feature is evident in the spatial spillover effect of DIA on EE. DIA impacts EE through its spatial effects on labor pooling, knowledge spillovers, and input sharing. The findings offer insights into the development of IA patterns and the enhancement of EE.

Research on the influence of mineral heterogeneity under different CO2 injection schemes in low permeability reservoirs

In the pursuit of sustainable oil and gas resource extraction, the innovative integration of carbon capture, utilization, and storage (CCUS) technology has emerged as the most promising approach. During the CCUS process, intricate physicochemical interactions between the injected CO2, facilitated through various injection strategies (Water Alternative Gas: WAG/Continue Gas Injection: CGI) and the formation fluids and heterogeneous mineral assemblages within the reservoir trigger alterations in mineral structures, consequently impacting permeability and recovery factors, constituting a pivotal aspect. Precisely delineating and quantifying these interactions is paramount for optimizing process design and evaluating reservoir dynamics in the successful implementation of CCUS operations. This study has carried out qualitative and quantitative characterization of mineral heterogeneity, different pore types, and mineral combination characteristics from a low-permeability sandstone reservoir. Additionally, the effect on the physical properties of minerals from different development methods (WAG/CGI) was investigated using numerical simulation for CCUS applications. The results indicate that the saturated CO2 fluid selectively dissolves the potassium feldspar (orthoclase) in intergranular pores, while the intergranular pores are filled with illite and secondary precipitated clay minerals. It initially dissolves the sensitive mineral (ankerite) in the intergranular pores. The decrease of ankerite and increase of illite result from the prolonged contact period between saturated CO2 and minerals, which changes the mineral cementation to argillaceous type, thus affecting permeability in the context of CCUS. The spatial impact on reservoir physical properties depends on the spatial heterogeneity of the original sensitive minerals (ankerite, anorthite, illite, etc.) distributed in the study area. In the WAG scheme, the physicochemical interaction between saturated CO2 and reservoir minerals is more intense than in the CGI scheme for CCUS operations, significantly impacting cumulative production.

Influence of Built-up Land and Vegetation Density to Urban Cool Island in Bandung City

Abstract Land use change is causing higher temperatures in urban centers, posing challenges for cities seeking livability and maintainability. Urban Cool Island can improve comfort in urban areas by directing microclimate and lowering temperature rise. The impact of Surface Urban Cool Island (SUCI) on a city’s ability to withstand urban microclimate phenomena is investigated in this study. This study aims to spatially analyze the spatial distribution, correlation, and impact of vegetation, surface temperature (LST), and built-up land density on Urban Cool Island Intensity. The method used was quantitative descriptive analysis of image data, including extraction of LST, NDVI, NDBI, and statistical analysis in the form of correlation and regression tests. The findings indicate that the Bandung City SUCI phenomena happened outside of the city at an intensity of -7.74°C to 0°C. Moderate vegetation density tends to be the cause of SUCI intensity, affecting 33.68%. Meanwhile, the density of built-up land has an effect of 43.18% on the intensity of SUCI. Correlation and regression analysis show that the relationship between SUCI intensity and NDVI is inversely proportional, while the relationship between NDBI and SUCI intensity is directly proportional. Denser buildings result in hotter SUCI intensity.

The spatial effect of low-carbon development of regional industries driven by the digital economy: evidence from Chinese cities

Utilizing data that encompass municipalities and regions within China at the prefectural level and beyond, spanning the period from 2012 to 2021, this study employed the spatial Durbin model to assess the spatial spillover impact of the digital economy in propelling low-carbon advancement within regional physical industries. This investigation elucidates the spatial spillover mechanism that underlies the low-carbon evolution of regional industries catalyzed by the digital economy and offers nuanced insights. The findings delineate the following observations: (1) The digital economy propels the low-carbon progression of indigenous physical industries and stimulates the low-carbon development of proximate regions’ physical industries through discernible spatial spillover effects. (2) The spatial spillover ramifications of the digital economy manifest a substantive correlation with urban attributes, including geographical positioning, population size, and market integration levels. Notably, municipalities situated in the southeast coastal region, those characterized by larger population sizes, and those exhibiting heightened market integration levels show greater spatial spillover effects attributable to the digital economy. (3) The safeguarding of intangible asset equity property, a pivotal institutional underpinning for fostering digital economic development, amplifies the spatial spillover effect of the digital economy in propelling low-carbon development within regional industries. (4) As geographical and spatial distances expand, the spatial spillover effect of the digital economy attenuates, indicating a diminishing influence with increasing spatial separation.

Study on the spatial nonlinear impact of haze pollution on residents’ health: a epidemiological perspective

Study on the spatial nonlinear impact of haze pollution on residents’ health: a epidemiological perspective

Agent-Based Model for Analyzing the Impact of Movement Factors of Sahelian Transhumant Herds

Transhumants move their herds based on strategies simultaneously considering several environmental and socio-economic factors. There is no agreement on the influence of each factor in these strategies. In addition, there is a discussion about the social aspect of transhumance and how to manage pastoral space. In this context, agent-based modeling can analyze herd movements according to the strategy based on factors favored by the transhumant. This article presents a reductionist agent-based model that simulates herd movements based on a single factor. Model simulations based on algorithms to formalize the behavioral dynamics of transhumants through their strategies. The model results establish that vegetation, water outlets and the socio-economic network of transhumants have a significant temporal impact on transhumance. Water outlets and the socio-economic network have a significant spatial impact. The significant impact of the socio-economic factor demonstrates the social dimension of Sahelian transhumance. Veterinarians and markets have an insignificant spatio-temporal impact. To manage pastoral space, water outlets should be at least 15km\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$15\\ km$$\\end{document} from each other. The construction of veterinary centers, markets and the securitization of transhumance should be carried out close to villages and rangelands.

Combining neural networks and data assimilation to enhance the spatial impact of Argo floats in the Copernicus Mediterranean biogeochemical model

Abstract. Biogeochemical-Argo (BGC-Argo) float profiles provide substantial information on key vertical biogeochemical dynamics and have been successfully integrated in biogeochemical models via data assimilation approaches. Although BGC-Argo assimilation results have been encouraging, data scarcity remains a limitation with respect to their effective use in operational oceanography. To address availability gaps in the BGC-Argo profiles, an observing system experiment (OSE) that combines a neural network (NN) and data assimilation (DA) was performed here. A NN was used to reconstruct nitrate profiles, starting from oxygen profiles and associated Argo variables (pressure, temperature, and salinity), while a variational data assimilation scheme (3DVarBio) was upgraded to integrate BGC-Argo and reconstructed observations in the Copernicus Mediterranean operational forecast system (MedBFM). To ensure the high quality of oxygen data, a post-deployment quality control method was developed with the aim of detecting and eventually correcting potential sensors drift. The Mediterranean OSE features three different set-ups: a control run without assimilation; a multivariate run with assimilation of BGC-Argo chlorophyll, nitrate, and oxygen; and a multivariate run that also assimilates reconstructed observations. The general improvement in the skill performance metrics demonstrated the feasibility of integrating new variables (oxygen and reconstructed nitrate). Major benefits have been observed with respect to reproducing specific biogeochemical-process-based dynamics such as the nitracline dynamics, primary production, and oxygen vertical dynamics. The assimilation of BGC-Argo nitrate corrects a generally positive bias of the model in most of the Mediterranean areas, and the addition of reconstructed profiles makes the corrections even stronger. The impact of enlarged nitrate assimilation propagates to ecosystem processes (e.g. primary production) at a basin-wide scale, demonstrating the importance of the assimilation of BGC-Argo profiles in forecasting the biogeochemical ocean state.