Kernel Density Model Research Articles

Refined Identification of Urban Functional Zones Integrating Multisource Data Features: A Case Study of Lanzhou, China

Identifying urban functional zones is one of the important foundational activities for urban renewal and the development of high-quality urban areas. Efficient and accurate identification methods for urban functional zones are significant for smart city planning and industrial layout optimization. However, existing studies have not adequately considered the impact of the interactions between human activities and geographical space provision on the delineation of urban functional zones. Therefore, from the perspective of integrating the spatiotemporal characteristics of human activities with the distribution of urban functional facilities, by incorporating mobile signaling, POI (point of interest), and building outline data, we propose a multifactorial weighted kernel density model that integrates ‘human activity–land feature area–public awareness’ to delineate urban functional zones quantitatively. The results show that the urban functional zones in the central city area of Lanzhou are primarily characterized by dominant single functional zones nested within mixed functional zones, forming a spatial pattern of ‘single–mixed’ synergistic development. Mixed function zones are widely distributed in the center of Lanzhou City. However, the area accounted for a relatively small proportion, the overall degree of functional mixing is not high, and the inter-district differences are obvious. The confusion matrix showed 85% accuracy and a Kappa coefficient of 0.83.

Mapping of Orogenic Gold Mineralization Potential in the Kushaka Schist Belt, Northcentral Nigeria: Insights from Point Pattern, Kernel Density, Staged-Factor, and Fuzzy AHP Modeling Techniques

Mapping of Orogenic Gold Mineralization Potential in the Kushaka Schist Belt, Northcentral Nigeria: Insights from Point Pattern, Kernel Density, Staged-Factor, and Fuzzy AHP Modeling Techniques

Spatial–Temporal Differentiation and Trend Prediction of Coupling Coordination Degree of Port Environmental Efficiency and Urban Economy: A Case Study of the Yangtze River Delta

Green development is a primary path for ports and cities to achieve a low-carbon transition under the Sustainable Development Goals and a powerful driving force to elevate regional port–city relations to a high level of coordination. In this paper, twenty port cities in the Yangtze River Delta (YRD) were selected and port environmental efficiency (PEE) was calculated through the window SBM model, while the EW-TOPSIS model was used to evaluate high-quality urban economic development (HED). The coupling coordination degree (CCD) model, the kernel density model, GIS spatial analysis, and the grey prediction model were used to further explore the spatial–temporal dynamic evolution and prediction of the CCD between PEE and HED. The results suggested that: (1) PEE fluctuation in the YRD is increasing, with a trend of seaports achieving higher PEE than river ports; (2) HED in the YRD shows upward trends, and the polarization of individual cities is obvious; (3) Temporally, the CCD in the YRD has risen from 0.438 to 0.518. Shanghai consistently maintains intermediate coordination, and Jiangsu has experienced the most significant increase in CCD. Spatially, CCD is led by Lianyungang, Suzhou, Shanghai, and Ningbo-Zhoushan, displaying a decreasing distribution pattern from east to west. The projection for 2026 suggests that all port cities within the YRD will have transitioned to a phase of orderly development. To enhance the coordination level in the YRD, policymakers should consider the YRD as a whole to position the ports functionally and manage them hierarchically, utilize the ports to break down resource boundaries to promote the synergistic division of labor among cities, and then tilt the resources towards Anhui.

The coordinated evolution of ecological environment, public service, and tourism economy along the Silk Road Economic Belt, using the Dual-Carbon Targets

Carbon peak and carbon neutrality (dual-carbon) are important targets for the international response to climate change. The Silk Road Economic Belt is a strategic resource region and is important for future ecological environment and tourism development. Based on the “dual-carbon” targets, the Single index quantification, Multiple index synthesis, and Poly-criteria integration evaluation model were used in this study to measure the coordinated development index of the ecological environment, public service, and tourism economy along the Silk Road Economic Belt and to analyze its spatial and temporal evolution. Further, it explores the dynamic evolution and development trend of the three systems using the Kernel Density and Grey Markov Prediction Model. The results show that the coordinated development index along this region needs to be improved during the study period. Furthermore, the coordinated development index of the Southwest region is relatively higher than that of the Northwest region. From the development trend of the three systems, all of them develop in a stable manner; however, the tourism economy system is easily affected by external disturbances. The coordinated development index of the three systems changes dynamically and tends to be in a good state of coordination. There is a certain spatial and temporal heterogeneity. The gravity center of the coordinated development index has been in the Southwest region. During the forecast period, the coordinated development index along this region will improve significantly, while insufficient and unbalanced development will continue.

Contradictions in human-nature relationships threaten coastal resilience and sustainability in the Bohai Rim Region, China.

Under the challenge of global environmental change and rapid development, tremendous risks brought about by natural disasters and human activities have increased environmental pressures for sustainable development. How to improve coastal resilience in the process of urban development has become an important topic in academia. In this study, a variable fuzzy recognition model was used to measure the level of coastal resilience in 17 cities in the Bohai Rim region, and then the kernel density, thiel index, and random forest model were used to explore the spatiotemporal characteristics and influencing factors of coastal resilience. The results show that (1) The overall resilience level of the Bohai Rim region is increasing over time, but at a relatively slow rate. (2) Coastal resilience has significant spatial unevenness, with high-level cities dominated by Tianjin, Qingdao, Yantai, etc. and low-level cities dominated by Cangzhou, Panjin, Yingkou, Binzhou, etc. (3) The influence of economic development, infrastructure, innovation ability, technology investment, and government regulation on coastal resilience decreases in order. Based on the research findings, the study can not only make suggestions for the actual regulation strategy but also provide empirical and theoretical experience for other coastal countries.

Does Urbanization Affect the Carbon-Output Efficiency of Agriculture? Empirical Evidence from the Yellow River Basin

Purpose: Improving agricultural carbon-output efficiency is an important path to realize the “double carbon” goal in the Yellow River Basin. In the context of rapid urbanization development, it is significant to explore whether promoting urbanization will affect agricultural carbon-output efficiency. Methods: Based on panel data of 75 cities in the Yellow River Basin from 2000 to 2020, this paper uses the super-DEA model, three-dimensional kernel density model, and Markov chain model to measure and analyze the spatio-temporal evolution of agricultural carbon-output efficiency in the Yellow River Basin. The panel Tobit model is used on this basis to analyze the relationship between urbanization and carbon-output efficiency in agriculture. Results: The results show the following: (1) The level of agricultural carbon-output efficiency in the Yellow River Basin is low and has not reached an effective state, showing a slow downward trend in general where the agricultural carbon-output efficiency in the lower reaches is higher than that in the middle reaches, and the upper reaches has the lowest. (2) Agricultural carbon-output efficiency in the Yellow River Basin has a negative trend of transitioning to a low level overall and maintaining its original level, and it is difficult to realize the leapfrog transfer between states. Agricultural carbon-output efficiency has an obvious spatial spillover effect and “club convergence” phenomenon; the high-efficiency area has a positive driving effect on the neighborhood area, while the low-efficiency area has a negative impact on the neighborhood area. (3) The level of urbanization has a significant positive impact on the carbon-output efficiency of agriculture in the upper, middle, and lower reaches of the Yellow River Basin, which plays an important role in promoting the green development of agriculture.

Spatial effects and influencing factors of urban sustainable development: An analysis of urban agglomerations in China

As the world becomes more environmentally conscious, the exploration of sustainable development paths for urban agglomerations has become a new platform for debate. This paper uses data from Chinese cities to study the level of urban sustainability. Three methods, kernel density, Gini coefficient and spatial measurement model, were used to explore the spatial effects and influencing factors of sustainable development of cities and urban agglomerations. Through the study, the following conclusions are obtained: First, the temporal and regional differences in sustainable urban development differ between cities belonging to urban agglomerations and those not belonging to urban agglomerations. In terms of overall distribution, there is a tendency for the eastern seaboard to radiate outwards. Second, the role of educational resources in urban sustainable development is particularly prominent. Finally, the sustainable development of urban agglomerations shows large spatial differences. The spillover effects of influencing factors vary from urban agglomeration to urban agglomeration. This study provides valuable guidance to policymakers and stakeholders. It can promote balanced and effective sustainable urban development.

Research on the Optimization Path of Green Development in Shaanxi Province Based on the OECD’s Perspective

The concept of green development proposed by the OECD has effectively promoted the coordinated development of economic growth, resource conservation, and environmental protection. In addition, it has provided effective ways and means of improving the quality of global social and economic development. Therefore, this study constructed the green development evaluation index system of Shaanxi Province based on the OECD green development concept, used the AHP and the entropy weight method for weighting, and measured the green development level of ten urban areas in Shaanxi Province from 2016 to 2021. Based on the score of the green development level, the LSE model, ESDA model, and kernel density model were introduced to decompose the optimization path of green development in Shaanxi Province from the three dimensions of contributing factors, spatial distribution, and temporal evolution. The specific research conclusions are: first, from 2016 to 2021, the green development score of Shaanxi Province was between 0.92 and 1.79, and the level of green development showed an overall upward trend, but the two-level differentiation trend between regions was serious. Second, the green development levels of each region in Shaanxi Province were three-factor type I, type II, four-factor type, and five-factor type. The influencing factors of each region showed different characteristics. Third, the level of green development among various regions in Shaanxi Province did not show spatial aggregation, and the development level was significantly different. The regional characteristics show that Xi’an is obviously superior to other regions and has not played a leading role. Fourth, over time, the situation of two-level differentiation in the ten city-level administrative districts of Shaanxi Province has eased, but the development situation is still very severe. The above research conclusions provide a reference and scientific basis for Shaanxi Province to formulate economic development policies and solve the problem associated with coordinated, sustainable development of the regional society-economy-environment.

Study on the Evaluation of the Development Efficiency of Smart Mine Construction and the Influencing Factors Based on the US-SBM Model

Taking the panel data of 13 provinces (autonomous regions and municipalities directly under the central government) in Shanxi and Xinjiang from 2011 to 2020 as the research object, we establish an evaluation index system for assessing smart mine construction development efficiency combined with the global reference method. The non-desired output super-efficiency slacks-based measure and the kernel density model were used to measure the development efficiency of smart mine construction and spatial structure evolution characteristics. This study explores the internal and external factors affecting the efficiency in various regions using the Tobit regression model. After conducting the analysis, the study obtained four main findings: (1) the development efficiency is influenced by the level of technology, and the overall level is low; (2) there are spatially heterogeneous and agglomerative characteristics, with large differences in regional distribution; (3) personnel is the main factor causing the phenomenon of severe redundancy in the region; and (4) the level of regional economic development, industrial structure, and the degree of government intervention are the main external factors that have a positive impact.

Parsing prehistoric patterns: Prospects and limitations of a big radiocarbon dataset for understanding the impact of climate on Late Palaeolithic and Mesolithic populations in northwest Europe (16–7.5 ka calBP)

Hunter-gatherer populations in northwest Europe were variably affected by Late Glacial and Early Holocene climate fluctuations and their effects on sea level and the environment. We investigate the impact of these fluctuations with a dates-as-data approach to a large radiocarbon dataset. Radiocarbon dates are used as a proxy for past human activity, the intensity, nature and archaeological visibility of which will indirectly influence date density. The significance of changes is explored using Kernel Density Estimates and model tested Summed Probability Distributions. Whereas previous studies have focused on smaller highly curated datasets to minimise research and preservation biases, our more inclusive approach maximises sample size, which is essential for these methods to reliably reflect underlying patterns. To deal with biases, we test subsets of the dataset that are potentially affected by differences in formation processes.The summed radiocarbon dataset follows the general fluctuations of climate conditions, showing increased activity in temperate periods and decreased activity during cold phases. Our results indicate significant periods of interest where the data deviates positively or negatively from our models. Notably we observe the impact of the Younger Dryas, Preboreal Oscillation and the 8.2 ka event on the density of hunter-gatherer activity. Additionally we see peaks in activity in our dataset during the Early and Late Boreal. Permutation testing of different regions in the research area shows these patterns are geographically differentiated.Our exploration of biasing factors indicates that we should be careful to interpret the abovementioned patterns, as different sampling processes and national policies may lie at the basis of several patterns. Furthermore, calibration artefacts may also cause issues at key parts of the timeline. Dates-as-data approaches require an understanding of the archaeology, the timing of external events, the impact of the calibration curve and how biases inherent to the dataset and research area may have influenced the formation of patterns in the result.

Spatio-Temporal Evolution of City Resilience in the Yangtze River Delta, China, from the Perspective of Statistics

The development of resilient cities has become a critical global issue with respect to the stimulation of sustainable economic, social, and ecological advancement. The Yangtze River Delta region, which is the most densely populated region in China, is undergoing the fastest urbanization and is achieving the highest level of economic development in the country. Thus, it is of great theoretical and practical significance to study the evolution of spatiotemporal city resilience in this region. For this study, the resilience of 41 core cities in the Yangtze River Delta in China from 2010 to 2020 was evaluated through a combination of game weighting and fuzzy matter-element analysis. Subsequently, the spatiotemporal differences in city resilience were revealed via the Dagum Gini coefficient and the Kernel density model. Further, the driving factors of city resilience were analyzed by a geographic detector model. The results revealed the following: (1) The resilience of the cities under study experienced a gradual upward trend (with Shanghai being consistently in the lead) and significant differences occurred between them. (2) The Dagum Gini coefficient indicated that the resilience of cities in the western portion of the Yangtze River Delta was quite diverse. This phenomenon was primarily due to the differences between sub-regions, for which the differences between the southeast and northwest were the most prominent. (3) The Kernel density indicated the absolute differences across the entire Delta as well as the northern sub-region, and there was a significant polarization phenomenon in the southern and western sub-regions. (4) Driving factor analysis revealed that the driving force of the income levels of residents was stronger and more stable, the driving force of economic development level was weakened, and the driving force of medical and health conditions, the degree of openness, and energy utilization efficiencies were strengthened. Overall, the driving factors of city resilience became more diversified and complex. Consequently, the Yangtze River Delta needs to improve city resilience levels in the northwest region in order to promote its balanced development. Our results suggested that more attention should be allocated to the improvement of the livelihoods of urban residents, the adjustment of energy consumption structures, and the optimization of the provision of medical resources.

Spatial-Temporal Characteristics and Influence Factors of Carbon Emission from Livestock Industry in China.

Animal husbandry is an important source of carbon emissions. As a large country, China must measure the carbon emissions from animal husbandry to reveal the spatial and temporal characteristics and determine the influencing factors to realize low-carbon animal husbandry and carbon emission reduction. In this paper, the carbon emissions of the livestock industry in each province of China were calculated with the emission coefficient method, considering the temperature change factor. The spatial and temporal characteristics and influencing factors of livestock industry carbon emissions were analyzed using the kernel density model, the spatial autocorrelation model, and the Tobit model. The results indicated that: (1) From 2000 to 2020, carbon emissions from the livestock industry in China experienced four stages: rapid rise, rapid decline, slow rise, and fluctuating decline, with an overall downward trend. Carbon emissions in the eastern and central regions showed a downward trend, while carbon emissions in the western regions showed an upward trend. (2) In terms of time, the relative gap in carbon emissions among the provinces narrowed first and then widened; the spatial agglomeration of carbon emissions from livestock farming in China increased, gradually forming the characteristics of "high agglomeration, low agglomeration", and showing a gradually decreasing pattern from northwest to southeast. (3) Nationwide, industrial structure, population, and farmers' income levels have had significantly promoting effects on animal husbandry carbon emissions, and the urbanization and agricultural mechanization levels have had significant inhibitory effects on carbon emissions. Finally, based on the above factors, it can be concluded that recognizing the location conditions, promoting the upgrading of industrial structures, and adopting differentiated strategies will help to promote the reduction in carbon emissions in animal husbandry and achieve its high-quality development.

Examining the Location Characteristics of Knowledge Industrial Space for Smart Planning and Industry 4.0: A Case Study of Hangzhou, China

In the era of Industry 4.0, the knowledge economy is reshaping the global economic structure, which makes the research on the layout of knowledge industries particularly important. This study, using Hangzhou in China as a case, constructs an index system from two dimensions (i.e., business and living amenities), and compares three typical representative knowledge industries. The nearest neighbor index, kernel density, and stepwise regression model were adopted. Results revealed that: (1) The spatial agglomeration intensity of knowledge industries is varied in different classes, with the financial industry being the most agglomerated, scientific research technology service industry the second, and smart manufacturing industry the least agglomerated. (2) The spatial distribution of knowledge industries is agglomerated in the shape of “#”, which is in line with the urban skeleton. (3) For the distribution of the financial industry, parking lots and cafés are strong influencing factors. The scientific research technology service industry locates closer to sports and fitness amenities, colleges and universities, and parks, while the smart manufacturing industry has a strong connection with snacking spots, fast food, and scientific research institutions. The results can provide a decision-making basis for the micro-location selection of urban knowledge industries and the adjustment of future industrial planning in the intelligent era.

Ancient Lowland Maya neighborhoods: Average Nearest Neighbor analysis and kernel density models, environments, and urban scale.

Many humans live in large, complex political centers, composed of multi-scalar communities including neighborhoods and districts. Both today and in the past, neighborhoods form a fundamental part of cities and are defined by their spatial, architectural, and material elements. Neighborhoods existed in ancient centers of various scales, and multiple methods have been employed to identify ancient neighborhoods in archaeological contexts. However, the use of different methods for neighborhood identification within the same spatiotemporal setting results in challenges for comparisons within and between ancient societies. Here, we focus on using a single method-combining Average Nearest Neighbor (ANN) and Kernel Density (KD) analyses of household groups-to identify potential neighborhoods based on clusters of households at 23 ancient centers across the Maya Lowlands. While a one-size-fits all model does not work for neighborhood identification everywhere, the ANN/KD method provides quantifiable data on the clustering of ancient households, which can be linked to environmental zones and urban scale. We found that centers in river valleys exhibited greater household clustering compared to centers in upland and escarpment environments. Settlement patterns on flat plains were more dispersed, with little discrete spatial clustering of households. Furthermore, we categorized the ancient Maya centers into discrete urban scales, finding that larger centers had greater variation in household spacing compared to medium-sized and smaller centers. Many larger political centers possess heterogeneity in household clustering between their civic-ceremonial cores, immediate hinterlands, and far peripheries. Smaller centers exhibit greater household clustering compared to larger ones. This paper quantitatively assesses household clustering among nearly two dozen centers across the Maya Lowlands, linking environment and urban scale to settlement patterns. The findings are applicable to ancient societies and modern cities alike; understanding how humans form multi-scalar social groupings, such as neighborhoods, is fundamental to human experience and social organization.

Geospatial characterization of rural settlements and potential targets for revitalization by geoinformation technology

To better implement the Strategy of Rural Revitalization, it is essential to characterize the rural settlements and understand their roles in the socio-environmental interactive system. This paper is hence aimed at achieving such a study using different spatial analysis such as kernel density and spatial autocorrelation (SA) and modeling approaches, e.g., simple and multiple linear regression analyses taking Jiangxi, a province in China as an example. Remote sensing, topographic and socioeconomic data were employed for this purpose. Through these analyses, it is found that the rural settlements in the study area appear to have a spatial distribution pattern of “dense north and sparse south” as an “F” type, and are quantitatively characterized as low elevations, flat terrain, high river and road densities, rich cultivated land resources and susceptible to the impact of urban radiation with a R2 of 0.520–0.748. Based on this understanding, a new inequality evaluation indicator of rural development, i.e., socio-environmental evaluation index (SEI), was developed. Areas with SEI lower than 0.40 should be given a priority to implement the revitalization strategy in the province. This index can also be extended to study of the imbalance of rural development in other regions and countries.

Spatial location differentiation and development decision optimization of characteristic villages and towns in China

Due to rapid urbanization and industrialization, the gap between urban and rural development has gradually increased. Rural development problems have been a significant topic of discussion, and are related to people's livelihoods. This article built a point-axis-region location driving system to analyze the spatial location differentiation of characteristic villages and towns (CVTS) using the kernel density model, and explored the mechanism of location driving factors with a geographical detector model. The results show that vegetables and fruits are the main types of products in CVTS. They account for 27.60% and 34.68% of all types of products, and occur mainly in the east and central regions of China. Moreover, all point-axis-region driving factors have a significant influence on grain crops. The mean values of driving forces of vegetables and fruits are larger than other types of CVTS, and their values are 0.12 and 0.11. The average driving forces on all CVTS in the northeast are higher than those in other regions, especially the driving forces of vegetables and medicinal crops (0.24 and 0.18, respectively). Finally, we proposed that the Chinese government should employ engineering technology, invest on road networks, e-commerce and blockchain technology to optimize the point-axis-region location advantages, to promote the sustainable development of CVTS. The detection of driving mechanisms on spatial location differentiation of CVTS has important research value for location theory and rural region systems research.

Link dynamic vehicle count estimation based on travel time distribution using license plate recognition data

This paper proposes a dynamic vehicle count estimation method for signalized links using license plate recognition (LPR) data considering the recognition errors. The framework contains three sub-components. First, travel time probability density function is estimated based on the kernel density model using the LPR data of matched vehicles; Second, the expected travel time is estimated for unmatched vehicles based on the travel time probability distribution; Third, with the complete arrival and departure information, the cumulative arrival-departure curve of the link is reconstructed, and the link dynamic vehicle count (LDVC) can be calculated correspondingly. To explore how different recognition errors affect LDVC estimation performance, the proposed model is validated through an empirical case of a real LPR dataset and a simulation case with different levels of matched rate. The results show that the model has a high level of estimation accuracy in different levels of matched rate of LPR data.

Spatio-Temporal Evolution and Spatial Heterogeneity of Influencing Factors of SO2 Emissions in Chinese Cities: Fresh Evidence from MGWR

In this study, based on the multi-source nature and humanities data of 270 Chinese cities from 2007 to2018, the spatio-temporal evolution characteristics of SO2 emissions are revealed by using Moran’s I, a hot spot analysis, kernel density, and standard deviation ellipse models. The spatial scale heterogeneity of influencing factors is explored by using the multiscale geographically weighted regression model to make the regression results more accurate and reliable. The results show that (1) SO2 emissions showed spatial clustering characteristics during the study period, decreased by 85.12% through pollution governance, and exhibited spatial heterogeneity of differentiation. (2) The spatial distribution direction of SO2 emissions’ standard deviation ellipse in cities was “northeast–southwest”. The gravity center of the SO2 emissions shifted to the northeast, from Zhumadian City to Zhoukou City in Henan Province. The results of hot spots showed a polarization trend of “clustering hot spots in the north and dispersing cold spots in the south”. (3) The MGWR model is more accurate than the OLS and classical GWR regressions. The different spatial bandwidths have a different effect on the identification of influencing factors. There were several main influencing factors on urban SO2 emissions: the regional innovation and entrepreneurship level, government intervention, and urban precipitation; important factors: population intensity, financial development, and foreign direct investment; secondary factors: industrial structure upgrading and road construction. Based on the above conclusions, this paper explores the spatial heterogeneity of urban SO2 emissions and their influencing factors, and provides empirical evidence and reference for the precise management of SO2 emission reduction in “one city, one policy”.

Sampling Sparse Representations with Randomized Measurement Langevin Dynamics

Stochastic Gradient Langevin Dynamics (SGLD) have been widely used for Bayesian sampling from certain probability distributions, incorporating derivatives of the log-posterior. With the derivative evaluation of the log-posterior distribution, SGLD methods generate samples from the distribution through performing as a thermostats dynamics that traverses over gradient flows of the log-posterior with certainly controllable perturbation. Even when the density is not known, existing solutions still can first learn the kernel density models from the given datasets, then produce new samples using the SGLD over the kernel density derivatives. In this work, instead of exploring new samples from kernel spaces, a novel SGLD sampler, namely, Randomized Measurement Langevin Dynamics (RMLD) is proposed to sample the high-dimensional sparse representations from the spectral domain of a given dataset. Specifically, given a random measurement matrix for sparse coding, RMLD first derives a novel likelihood evaluator of the probability distribution from the loss function of LASSO, then samples from the high-dimensional distribution using stochastic Langevin dynamics with derivatives of the logarithm likelihood and Metropolis–Hastings sampling. In addition, new samples in low-dimensional measuring spaces can be regenerated using the sampled high-dimensional vectors and the measurement matrix. The algorithm analysis shows that RMLD indeed projects a given dataset into a high-dimensional Gaussian distribution with Laplacian prior, then draw new sparse representation from the dataset through performing SGLD over the distribution. Extensive experiments have been conducted to evaluate the proposed algorithm using real-world datasets. The performance comparisons on three real-world applications demonstrate the superior performance of RMLD beyond baseline methods.

Central European Early Bronze Age chronology revisited: A Bayesian examination of large-scale radiocarbon dating.

In archaeological research, changes in material culture and the evolution of styles are taken as major indicators for socio-cultural transformation. They form the basis for typo-chronological classification and the establishment of phases and periods. Central European Bronze Age material culture from burials reveals changes during the Bronze Age and represents a perfect case study for analyzing phenomena of cultural change and the adoption of innovation in the societies of prehistoric Europe. Our study focuses on the large-scale change in material culture which took place in the second millennium BC and the emergence at the same period of new burial rites: the shift from inhumation burials in flat graves to complex mounds and simple cremation burials. Paul Reinecke was the first to divide the European Bronze Age (EBA) into two phases, Bz A1 and A2. The shift from the first to the second phase has so far been ascribed to technical advances. Our study adopted an innovative approach to quantifying this phenomenon. Through regressive reciprocal averaging and Bayesian analysis of radiocarbon-dated grave contexts located in Switzerland and southern Germany, we modelled chronological changes in the material culture and changes in burial rites in these regions in a probabilistic way. We used kernel density models to summarize radiocarbon dates, with the aim of visualizing cultural changes in the third and second millennium BC. In 2015, Stockhammer et al. cast doubt on the chronological sequence of the Reinecke phases of the EBA on the basis of newly collected radiocarbon dates from southern Germany. Our intervention is a direct response to the results of that study. We fully agree with Stockhammer’s et al. dating of the start of EBA, but propose a markedly different dating of the EBA/MBA transition. Our modelling of radiocarbon data demonstrates a statistically significant typological sequence of phases Bz A1, Bz A2 and Bz B and disproves their postulated chronological overlap. The linking of the archaeological relative-chronological system with absolute dates is of major importance to understanding the temporal dimension of the EBA phases.