Local Indicators Of Spatial Association Research Articles

Evaluation of Four Satellite Precipitation Products over Mainland China Using Spatial Correlation Analysis

The accuracy and reliability of satellite precipitation products (SPPs) are important for their applications. In this study, four recently presented SPPs, namely, GSMaP_Gauge, GSMaP_NRT, IMERG, and MSWEP, were evaluated against daily observations from 2344 gauges of mainland China from 2001 to 2018. Bivariate Moran’s I (BMI), a method that has demonstrated high applicability in characterizing spatial correlation and dependence, was first used in research to assess their spatial correlations with gauge observations. Results from four conventional indices indicate that MSWEP exhibited the best performance, with a correlation coefficient of 0.78, an absolute deviation of 1.6, a relative bias of −5%, and a root mean square error of 5. Six precipitation indices were selected to further evaluate the spatial correlation between the SPPs and gauge observations. MSWEP demonstrated the best spatial correlation in annual total precipitation, annual precipitation days, continuous wet days, continuous dry days, and very wet day precipitation with global BMI of 0.95, 0.78, 0.78, 0.78, and 0.87, respectively. Meanwhile, IMERG showed superiority in terms of maximum daily precipitation with a global BMI value of 0.91. IMERG also exhibited superior performance in quantifying the annual count days that experience precipitation events exceeding 25 mm and 50 mm, with a global BMI of 0.96, 0.92. In four sub-regions, these products exhibited significant regional characteristics. MSWEP demonstrated the highest spatial correlation with gauge observations in terms of total and persistent indices in the four sub-regions, while IMERG had the highest global BMI for extreme indices. In general, global BMI can quantitatively compare the spatial correlation between SPPs and gauge observations. The Local Indicator of Spatial Association (LISA) cluster map provides clear visual representation of areas that are significantly overestimated or underestimated. These advantages make BMI a suitable method for SPPs assessment.

Spatial analysis for highly multiplexed imaging data to identify tissue microenvironments.

Highly multiplexed in situ imaging cytometry assays have made it possible to study the spatial organization of numerous cell types simultaneously. We have addressed the challenge of quantifying complex multi-cellular relationships by proposing a statistical method which clusters local indicators of spatial association. Our approach successfully identifies distinct tissue architectures in datasets generated from three state-of-the-art high-parameter assays demonstrating its value in summarizing the information-rich data generated from these technologies.

Spatiotemporal characteristic and evolution of China's marine economic resilience

Resilience has become an essential aspect of the high-quality development of the marine economy. This study considered multiple combinations of dimensions, including politics, economy, culture, society, ecology, and security, in developing marine economy resilience. Based on the adaptability theory, this study measures the resilience of China's marine economy by constructing an evaluation index system using the three resilience sub-dimensions of resistance, adaptability, and evolution from 2000 to 2020 in 11 Chinese coastal provinces. The evaluation index system also considers social factors such as institution, culture, and history. The new technical methods such as remote sensing interpretation and text analysis are used to obtain one-hand data. We use a fuzzy comprehensive evaluation (FCE) model to calculate the comprehensive evaluation index and determine the weight of each index using the entropy weight method, analytic hierarchy process, and the least square decision model. Furthermore, based on the Local Indicators of Spatial Association (LISA) time path and spatiotemporal transition probability matrix, we analyze the spatiotemporal characteristic evolution characteristics of marine economic resilience. The results reveal that (1) the resilience, resistance, adaptability, and evolution of the marine economy in 11 coastal provinces show an upward trend from 2000 to 2020. (2) The development speed of the resilience level differs in terms of resistance, adaptability, and evolution, but the overall level shows adaptability > evolution > resistance. (3) The analysis of the spatiotemporal interaction characteristics of resilience shows that the overall spatial characteristic of resilience is relatively stable. Therefore, some measurements are suggested to improve marine economic resilience. For example, government formulates marine plans, sea-related enterprises assume social responsibilities, and individuals actively participate in marine governance. The evaluation model of marine economic resilience applies to other complex systems with multiple factors.

Spatial Concentration of Health Industry under COVID-19.

The COVID-19 pandemic has impacted the industry immensely and, in some cases, irreversibly. This research pioneers in studying how the pandemic have influenced the survival and spatial distribution of the health-related manufacturing industry (HRMI) in Taiwan. Eight categories of HRMI are examined, with their change in survival performances and spatial concentration between 2018 and 2020. Average Nearest Neighbour and Local Indicators of Spatial Association are conducted, to visualise the distribution of industrial clusters. We found the pandemic did not shock the HRMI in Taiwan, but actually induced its growth and spatial concentration to a certain extent. Additionally, due to it being a knowledge-intensive industry, the HRMI mainly concentrate in metropolitan areas with which universities and science parks may have largely supported. However, the spatial concentration and cluster scope growth do not necessarily accompany the improvement of spatial survival, which may be resulted from the different life cycle stages an industry category is in. This research fills in the gap of medical studies with literatures and data from the field of spatial studies. It provides interdisciplinary insights under the condition of pandemic.

Pola Sebaran Spasial Stunting di Kabupaten Lampung Selatan dengan Pendekatan Autokorelasi Spasial

Stunting is a condition in toddlers who experience failure to thrive due to chronic malnutrition which causes children to be shorter than their cohort. The Indonesian Ministry of Health's Basic Health Research in 2018 reported that the prevalence of stunting in the South Lampung Regency was above the WHO standard of 20%, although it decreased from 43.01% in 2013 to 29.08% in 2018. This study aims to identify spatial distribution pattern of stunting prevalence in the South Lampung Regency. The methods included Moran's Index, Moran's Scatterplot, Local Indicator of Spatial Association (LISA) and correlation analysis. The results showed that there was a spatial autocorrelation to the prevalence of stunting in South Lampung Regency with the spatial distribution pattern being clustered. The grouping of areas in the high-high (HH) cluster is mostly located in villages in Kalianda District with one of the contributing factors being the joint use of water from inappropriate sources. Clean water provision is expected to reduce stunting prevalence in the South Lampung Regency.

Uneven geographies: ageing and population dynamics in Latvia

AbstractThe twin phenomena of ageing populations and declining populations are profoundly transforming societies and economies in Europe. Driven by decreasing fertility rates and the continuing increased life expectancy, populations have not been ageing uniformly across time and space. In an increasingly urbanized world, the spatial distribution of the young and the elderly is a matter of growing scholarly and policy interest. In Europe, this process is more pronounced in peripheral areas than in the core regions, while the development of metropolitan areas is associated with suburbanization predominantly driven by young in‐migrants. Latvia is an interesting case study for studying population ageing and its spatial imbalances due to the key role of emigration in population decline and ageing. By employing descriptive and spatial analysis, this study uses data from the Latvian census and population register to explore the relationships between population change and ageing processes. Global Moran's I and the local indicators of spatial association were used to identify spatial systems. The study confirmed global and local spatial autocorrelation for the both examined age variables. Using the Getis‐Ord Gi* method identified spatial clusters of the young and the elderly. We found more pronounced residential clustering of the elderly aged 75 and over in the depopulating region of Latgale, whereas the Riga metropolitan region and areas around the largest towns tend to cluster young residents.

Integrating spatial clustering with predictive modeling of pipe failures in water distribution systems

ABSTRACT Pipe failures in water distribution infrastructure (WDI) have significant economic, environmental and public health impacts. To alleviate these impacts, repair and replacement decisions need to be prioritized to effectively reduce failure rates. In this study, a computational framework is proposed for WDI asset management that couples spatial clustering analysis with predictive modeling of pipe failures. First, hotspot/coldspot clusters of statistically significant high/low failure rates are identified using local indicators of spatial association. Second, the predictive abilities of eight statistical learning techniques are systematically tested, and the best-performing method is implemented to forecast failure rates,(breaks/(km.year)) within different sectors of the WDI. Third, the framework is implemented to compare the impact of adopting proactive instead of reactive pipe replacement strategies. Applying the framework to a real-life, large-scale WDI revealed that spatial clustering of pipe failures improves the accuracy of the prediction models.

Analisis Autokorelasi Spasial Global dan Lokal Pada Data Kemiskinan Provinsi Bali

Poverty is a crucial problem that often occurs in Indonesia, including in the province of Bali. The poverty rate rate in Bali has increased significantly over the last 3 years, so further analysis is needed so that this poverty rate can be reduced. The pattern of poverty between regions is different because it depends on the geographical and socio-cultural conditions in each area. The effect of location on poverty cases can be identified by global and local spatial autocorrelation methods. This study aimed to analyze the spatial autocorrelation of poverty data in Bali Province using the global autocorrelation test with the Moran’s and Geary’s C indices as well as the local spatial autocorrelation test with the Local Indicator of Spatial Association (LISA) and Getis-Ord G to obtain an overview of the spatial distribution of poverty data. Based on the global autocorrelation test, it is concluded that using Moran’s index there is a negative spatial autocorrelation in the 2020-2022 data for a=10%. Similar results were also obtained when using the Geary’s C test. In the local autocorrelation test using LISA, it was found that districts had negative spatial autocorrelation, namely in 2020 Buleleng Regency (high-low) and Klungkung (low-low), for 2021 there is Buleleng Regency (high-low) and Jembrana (low-low), while for 2022 only Buleleng Regency (high-low) has negative spatial autocorrelation. For local autocorrelation testing with the Getis-Ord G test, it was found that there were no districts/cities that showed spatial grouping or that there was no spatial autocorrelation locally.

Accurate Prediction of Soil Heavy Metal Pollution Using an Improved Machine Learning Method: A Case Study in the Pearl River Delta, China.

In traditional soil heavy metal (HM) pollution assessment, spatial interpolation analysis is often carried out on the limited sampling points in the study area to get the overall status of heavy metal pollution. Unfortunately, in many machine learning spatial information enhancement algorithms, the additional spatial information introduced fails to reflect the hierarchical heterogeneity of the study area. Therefore, we designed hierarchical regionalization labels based on three interpolation techniques (inverse distance weight, ordinary kriging, and trend surface interpolation) as new spatial covariates for a machine learning (ML) model. It was demonstrated that regional spatial information improved the prediction performance of the model (R2 > 0.7). On the basis of the prediction results, the status of HM pollution in the Pearl River Delta (PRD) region was evaluated: cadmium (Cd) and copper (Cu) were the most serious pollutants in the PRD (the point overstandard rates are 18.77% and 12.95%, respectively). The analysis of index importance and bivariate local indicators of spatial association (LISA) shows that the key factors affecting the spatial distribution of heavy metals are geographical and climatic conditions [namely, altitude, humidity index, and normalized vegetation difference index (NDVI)] and some industrial activities (such as metal processing, printing and dyeing, and electronics industry). This study develops a novel approach to improve existing spatial interpolation techniques, which will enable more precise and scientific soil environmental management.

Different responses of Japanese encephalitis to weather variables among eight climate subtypes in Gansu, China, 2005–2019

This study evaluated epidemic temporal aspects of Japanese encephalitis (JE) and investigated the weather threshold of JE response across eight climate subtypes between 2005 and 2019 in Gansu Province, China. Epidemiological data were collected from the China Information System for Disease Control and Prevention (CISDCP). Three epidemic temporal indices [frequency index (α), duration index (β), and intensity index (γ)] were adopted for the comparison of epidemic features among different climate subtypes. In addition, the local indicators of spatial association (LISA) technique was used to detect the hot-spot areas. The category and regression tree (CART) model was used to detect the response threshold of weather variables in hot-spot areas across climate subtypes. Among eight climate subtypes in Gansu, in most hot-spot areas (i.e., high–high clusters), α, β, and γ were detected in the climate subtypes of subtropical winter dry (Cwa), temperate oceanic continental (Cwb), and continental winter dry (Dwa and Dwb). According to the CART analysis, a minimum monthly temperature is required for Japanese encephalitis virus (JEV) transmission, with different threshold values among the climatic subtypes. In temperate climate zones (Cwa and Cwb), this threshold is 19 °C at a 1-month lag. It is lower in continental winter dry climate zones: 18 °C in Dwa (snow climate, dry winter, and hot summer) and 16 °C in Dwb (snow climate, dry winter, and warm summer). Additionally, some areas of the areas with temperate arid (BWk and BSk) had the first JE cases. Further studies to detect whether the climate change influence the JEV’s distribution in Gansu Province are needed.

A Study on the Spatial Pattern of the Ecological Product Value of China’s County-Level Regions Based on GEP Evaluation

Gross Ecosystem Product (GEP) is a concept that reflects ecological product value by using geospatial technology. It can demonstrate the spatial distribution of ecological products and provide new perspectives and refined support for spatial planning. China's county-level regions are important units for the promotion of ecological product value. Based on the concept of GEP, this study evaluated the ecological product value of China's county-level regions in 2020, used Local Indicators of Spatial Association (LISA) to visualise spatial patterns and conducted a correlation analysis between the GEP indices and economic and land use factors. The study found that the results of evaluation and analysis varied by spatial distribution: (1) county-level regions with high provisioning service indices are concentrated in northeastern China and southeastern China; (2) county-level regions with high regulating service indices are concentrated south of the Yangtze River and in the southern region of the Qinghai-Tibet Plateau; (3) county-level regions with high cultural service indices are concentrated in southeastern China; (4) county-level regions with high composite GEP indices are concentrated in northeastern China. The results have different correlations with different factors, reflecting the complex mechanisms behind ecological value transformation. For example, the composite GEP index for an area has a strong positive correlation with the area's proportions of woodland area, water area and GDP.

Spatial Pattern of Stunting on Children under Five in Indonesia 2019

Background: The stunting countermeasures is one of the agendas in SDGs. The strategic and appropriate attempts could be planned based on the distribution of stunting and the influencing factors using a region-based data approach in accordance with the critical steps within the 2018 Global Nutrition Report. 
 Objective: The purpose of this study was to determine the spatial autocorrelation of stunting on children under five between districts/cities in Indonesia 2019. 
 Method: This research is an ecological study using a spatial approach. The data used was secondary data obtained from the study of Nutritional Status of children in 2019, with the unit of analysis for all districts/cities in Indonesia. The study was tested using the analysis of Global Moran’s I and Local Indicators of Spatial Association (LISA). 
 Result: The result of the analysis showed that stunting on children under five between districts/cities in Indonesia has a spatial autocorrelation with a clustered pattern (I=0.347). 
 Conclusion: Areas with high-high stunting patterns need to be set as priority areas for stunting reduction interventions. In addition, the formation of a Stunting Reduction Acceleration Team in these areas needs to be monitored thoroughly.

Spatial and deep learning analyses of urban recovery from the impacts of COVID-19

This study investigates urban recovery from the COVID-19 pandemic by focusing on three main types of working, commercial, and night-life activities and associating them with land use and inherent socio-economic patterns as well as points of interests (POIs). Massive multi-source and multi-scale data include mobile phone signaling data (500 m × 500 m), aerial images (0.49 m × 0.49 m), night light satellite data (500 m × 500 m), land use data (street-block), and POIs data. Methods of convolutional neural network, guided gradient-weighted class activation mapping, bivariate local indicator of spatial association, Elbow and K-means are jointly applied. It is found that the recovery in central areas was slower than in suburbs, especially in terms of working and night-life activities, showing a donut-shaped spatial pattern. Residential areas with mixed land uses seem more resilient to the pandemic shock. More than 60% of open spaces are highly associated with recovery in areas with high-level pre-pandemic social-economic activities. POIs of sports and recreation are crucial to the recovery in all areas, while POIs of transportation and science/culture are also important to the recovery in many areas. Policy implications are discussed from perspectives of open spaces, public facilities, neighborhood units, spatial structures, and anchoring roles of POIs.

Spatiotemporal Dynamic Evolution and Its Driving Mechanism of Carbon Emissions in Hunan Province in the Last 20 Years.

Global warming caused by carbon emissions is an environmental issue of great concern to all sectors. Dynamic monitoring of the spatiotemporal evolution of urban carbon emissions is an important link to achieve the regional "double carbon" goal. Using 14 cities (prefectures) in Hunan Province as an example, based on the data of carbon emissions generated by land use and human production and life, and on the basis of estimating the carbon emissions in Hunan Province from 2000 to 2020 using the carbon emission coefficient method, this paper uses the Exploratory Spatial-Temporal Data Analysis (ESTDA) framework to analyze the dynamic characteristics of the spatiotemporal pattern of carbon emissions in Hunan Province from 2000 to 2020 through the Local Indicators of Spatial Association (LISA) time path, spatiotemporal transition, and the standard deviation ellipse model. The driving mechanism and spatiotemporal heterogeneity of urban carbon emissions were studied by using the geographically and temporally weighted regression model (GTWR). The results showed that: (1) In the last 20 years, the urban carbon emissions of Hunan Province have had a significant positive spatial correlation, and the spatial convergence shows a trend of first increasing and then decreasing. Therefore, priority should be given to this relevance when formulating carbon emission reduction policies in the future. (2) The center of carbon emission has been distributed between 112°15'57″~112°25'43″ E and 27°43'13″~27°49'21″ N, and the center of gravity has shifted to the southwest. The spatial distribution has changed from the "northwest-southeast" pattern to the "north-south" pattern. Cities in western and southern Hunan are the key areas of carbon emission reduction in the future. (3) Based on LISA analysis results, urban carbon emissions of Hunan from 2000 to 2020 have a strong path dependence in spatial distribution, the local spatial structure has strong stability and integration, and the carbon emissions of each city are affected by the neighborhood space. It is necessary to give full play to the synergistic emission reduction effect among regions and avoid the closure of inter-city emission reduction policies. (4) Economic development level and ecological environment have negative impacts on carbon emissions, and the population, industrial structure, technological progress, per capita energy consumption, and land use have a positive impact on carbon emissions. The regression coefficients are heterogeneous in time and space. The actual situation of each region should be fully considered to formulate differentiated emission reduction policies. The research results can provide reference for the green and low-carbon sustainable development of Hunan Province and the formulation of differentiated emission reduction policies, and provide reference for other similar cities in central China.

Use of spatial panel-data models to investigate factors related to incidence of end-stage renal disease: a nationwide longitudinal study in Taiwan

BackgroundThe assumptions of conventional spatial models cannot estimate the responses across space and over time. Here we propose new spatial panel data models to investigate the association between the risk factors and incidence of end-stage renal disease (ESRD).MethodsA longitudinal (panel data) study was conducted using data from the National Health Insurance Database in Taiwan. We developed an algorithm to identify the patient’s residence and estimate the ESRD rate in each township. Corresponding covariates, including patient comorbidities, history of medication use, and socio-environmental factors, were collected. Local Indicators of Spatial Association were used to describe local spatial clustering around an individual location. Moreover, a spatial panel data model was proposed to investigate the association between ESRD incidence and risk factors.ResultsIn total, 73,995 patients with ESRD were included in this study. The western region had a higher proportion of high incidence rates than the eastern region. The proportion of high incidence rates in the eastern areas increased over the years. We found that most “social environmental factors,” except average income and air pollution (PM 2.5 and PM10), had a significant influence on the incidence rate of ESRD when considering spatial dependences of response and explanatory variables. Receiving non-steroidal anti-inflammatory drugs and aminoglycosides within 90 days prior to ESRD had a significant positive effect on the ESRD incidence rate.ConclusionFuture comprehensive studies on townships located in higher-risk clusters of ESRD will help in designing healthcare policies for suitable action.

Three-dimensional distribution characteristics of multiple pollutants in the soil at a steelworks mega-site based on multi-source information

Soil pollution at steelworks mega-sites has become a severe environmental issue worldwide. However, due to the complex production processes and hydrogeology, the soil pollution distribution at steelworks is still unclear. This study scientifically cognized the distribution characteristics of polycyclic aromatic hydrocarbons (PAHs), volatile organic compounds (VOCs), and heavy metals (HMs) at a steelworks mega-site based on multi-source information. Specifically, firstly, 3D distribution and spatial autocorrelation of pollutants were obtained by interpolation model and local indicators of spatial associations (LISA), respectively. Secondly, the characteristics of horizontal distribution, vertical distribution, and spatial autocorrelations of pollutants were identified by combining multi-source information such as production processes, soil layers, and properties of pollutants. Horizontal distribution showed that soil pollution in steelworks mainly occurred in the front end of the steel process chain. Over 47% of PAHs and VOCs pollution area were distributed in coking plants and over 69% of HMs in stockyards. Vertical distribution indicated that HMs, PAHs, and VOCs were enriched in the fill, silt, and clay layers, respectively. Spatial autocorrelation of pollutants was positively correlated with their mobility. This study clarified the soil pollution characteristics at steelworks mega-sites, which can support the investigation and remediation of steelworks mega-sites.

Simultaneous Inequity of Elderly Residents in Melbourne Metropolitan

The importance of proper access to urban amenities for elderly residents is evidenced in the literature. Among them, mobility infrastructure, particularly public transport (PT), is of pivotal significance due to its intermediary role in access to other amenities such as healthcare or urban green space (UGS). Given this, the inequity in access to PT could lead to more adverse impacts on society, especially if it coincides with inequities in other amenities. In response, we calculate local indicators of spatial association (LISA) between the elderly population and urban amenities of PT, healthcare, and UGS at the suburban level of Melbourne Metropolitan. We, then, introduce and develop a LISA-on-LISA model to analyze and reveal the coexistence of inequities at the suburban level. The results evidence the existence of inequity in access to PT for elderly cohorts. We further reveal the clusters of PT inequity which are, at the same time, experiencing healthcare and UGS inequities. The implications of the study for resource allocation and distribution in areas suffering from simultaneous inequity are discussed.

Essential childhood immunization in 43 low- and middle-income countries: Analysis of spatial trends and socioeconomic inequalities in vaccine coverage.

Globally, access to life-saving vaccines has improved considerably in the past 5 decades. However, progress has started to slow down and even reverse in recent years. Understanding subnational heterogeneities in essential child immunization will be critical for closing the global vaccination gap. We use vaccination information for over 220,000 children across 1,366 administrative regions in 43 low- and middle-income countries (LMICs) from the most recent Demographic and Health Surveys. We estimate essential immunization coverage at the national and subnational levels and quantify socioeconomic inequalities in such coverage using adjusted concentration indices. Within- and between-country variations are summarized via the Theil index. We use local indicator of spatial association (LISA) statistics to identify clusters of administrative regions with high or low values. Finally, we estimate the number of missed vaccinations among children aged 15 to 35 months across all 43 countries and the types of vaccines most often missed. We show that national-level vaccination rates can conceal wide subnational heterogeneities. Large gaps in child immunization are found across West and Central Africa and in South Asia, particularly in regions of Angola, Chad, Nigeria, Guinea, and Afghanistan, where less than 10% of children are fully immunized. Furthermore, children living in these countries consistently lack all 4 basic vaccines included in the WHO's recommended schedule for young children. Across most countries, children from poorer households are less likely to be fully immunized. The main limitations include subnational estimates based on large administrative divisions for some countries and different periods of survey data collection. The identified heterogeneities in essential childhood immunization, especially given that some regions consistently are underserved for all basic vaccines, can be used to inform the design and implementation of localized intervention programs aimed at eliminating child suffering and deaths from existing and novel vaccine-preventable diseases.

Detection of hotspots of school dropouts in India: A spatial clustering approach.

School dropout is a significant concern universally. This paper investigates the incorporation of spatial dependency in estimating the topographical effect of school dropout rates in India. This study utilizes the secondary data on primary, upper primary, and secondary school dropout rates of the different districts of India available at the Unified District Information System for Education plus (UDISE+) for the year 2020 to contemplate the impact of these dropouts from one region to different regions in molding with promotion rate and repetition rate. The Global Moran's I, Univariate and Bivariate Local Indicators of Spatial Association, and spatial models are utilized to investigate the geographical variability and to find the possible relationship between dropout rates and the school-level factors at the district level. The outcomes provide clear spatial clustering and precisely highlight the hot zone dropout regions with high repetition and low promotion rates. Based on this study's results, educational administrators can make evidence-based decisions to reduce dropout rates in hot zones of various regions of India. Furthermore, futuristic studies focusing on linking spatial hot zones with causal factors will add consistent data in assisting policymakers in taking necessary measures to develop a sound education management system.

Poultry manure gleaned antibiotic residues in soil environment: A perspective of spatial variability and influencing factors

The antibiotics released by human and animals end up in the environmental sinks like soil and water to cause contamination and induce resistance in the microflora. The knowledge of fate and behavior of antibiotics in diverse geographical, climatological, and physicochemical is limited. Therefore, present study investigated the spatial distribution of antibiotics and their relationship with various factors and the source-sink relationship between soil and poultry manure. This was achieved by employing spatially constrained hierarchical clustering, global and local spatial autocorrelation, and spatial regression techniques. Most of the antibiotics co-occurred in both soil and poultry manure matrices, however antibiotic concentration in soil (1.20 μg kg−1 < antibiotics ≤21.38 μg kg−1) was lower than that in the poultry manure (7.05 μg kg−1< antibiotics ≤60.2 μg kg−1). Majority of the antibiotics showed spatial independence in both poultry manure and soil, except for sulfadiazine, sulfanilamide and sulfapyridine with Moran's I > − 0.111. Local indicator of spatial association indicated localized spatial clustering and outlier behavior of antibiotics. The underlying reasons for spatial heterogeneity of antibiotics resolved by spatial regression models indicated elevation, S%, C%, pH and mean annual temperature as the major factors. The influence of antibiotic concentration in poultry manure as a source was significant but marginal compared to the other predictors of spatial heterogeneity.