Law Of Geography Research Articles

An intelligent modeling framework to optimize the spatial layout of ocean moored buoy observing networks

This research is motivated by the practical requirements in the sustainable deployment of ocean moored buoy observing networks. Ocean moored buoys play an important role in the global marine environment monitoring. Ocean buoy station layout planning is a typical multiple-objective spatial optimization problem that aims to reduce the spatial correlation of buoy stations and improve their spatial monitoring efficiency. In this paper, we develop a multi-objective mathematical model for allocating ocean buoy stations (MOLMofOBS) based on Tobler’s first law of geography. A spatial neighborhood model based on a Voronoi diagram is built to represent the spatial proximity of distributed buoy stations and delimit the effective monitoring region of every station. Then, a heuristic method based on a multiple-objective particles swarm optimization (MOPSO) algorithm is developed to calculate the MOLMofOBS via a dynamic inertia weight strategy. Meanwhile, a series of experiments is conducted to verify the efficiency of the proposed model and algorithms in solving single- and multiple-buoy station location problems. Finally, an interactive portal is developed in the Cyberinfrastructure environment to provide decision-making services for online real-time planning of the ocean buoy station locations. The work reported in this paper will provide spatial decision-making support for the sustainable development of ocean buoy observing networks.

The breaking points of human development-the separating role of national borders

The human development index (HDI) has been used for a long time for the complex comparison of individual countries (UNDP 2023). According to Waldo Tobler's “First Law of Geography,” the data of each country should theoretically be similar to the values of neighboring countries. At least if land borders are taken into account, since the role of maritime borders is difficult to interpret in terms of similarity between countries. In the following figure, I examine the differences in the human development index of each country, which I consider to be the breaking points of development. In the case of countries that have a land border between them, I look at the difference in the index and plot the border in terms of the difference. Of course, the difference is not only important in terms of size, but also in the extent to which countries with populations can be observed. Therefore, I modified the area of the countries in proportion to their 2023 population. The source of the data is worldometers.info, while the map work was created using the ScapeToad software.

Leveraging the metacoupling framework for sustainability science and global sustainable development.

Sustainability science seeks to understand human-nature interactions behind sustainability challenges, but has largely been place-based. Traditional sustainability efforts often solved problems in one place at the cost of other places, compromising global sustainability. The metacoupling framework offers a conceptual foundation and a holistic approach to integrating human-nature interactions within a place, as well as between adjacent places and between distant places worldwide. Its applications show broad utilities for advancing sustainability science with profound implications for global sustainable development. They have revealed effects of metacoupling on the performance, synergies, and trade-offs of United Nations Sustainable Development Goals (SDGs) across borders and across local to global scales; untangled complex interactions; identified new network attributes; unveiled spatio-temporal dynamics and effects of metacoupling; uncovered invisible feedbacks across metacoupled systems; expanded the nexus approach; detected and integrated hidden phenomena and overlooked issues; re-examined theories such as Tobler's First Law of Geography; and unfolded transformations among noncoupling, coupling, decoupling, and recoupling. Results from the applications are also helpful to achieve SDGs across space, amplify benefits of ecosystem restoration across boundaries and across scales, augment transboundary management, broaden spatial planning, boost supply chains, empower small agents in the large world, and shift from place-based to flow-based governance. Key topics for future research include cascading effects of an event in one place on other places both nearby and far away. Operationalizing the framework can benefit from further tracing flows across scales and space, uplifting the rigor of causal attribution, enlarging toolboxes, and elevating financial and human resources. Unleashing the full potential of the framework will generate more important scientific discoveries and more effective solutions for global justice and sustainable development.

A methodology to quantify the neighborhood decay effect of urban cellular automata models

Spatial heterogeneity is an essential feature of land use change. It is important for cellular automata (CA)-based urban expansion modeling to consider the spatial heterogeneity of neighborhood effect. According to the first law of geography, there exists a distance decay effect in the neighborhood space of the urban CA models. But no research has ever been able to accurately quantify the neighborhood decay effect and apply it to models. In this study, we propose a methodology to accurately quantify the neighborhood decay effect of CA models with large neighborhood size, including the design of neighborhood configuration and the selection and optimization of decay strategies. Taking Wuhan and Guangzhou as examples, we show the implementation process of the methodology and simulate urban expansion of two areas for the periods 2000–2010 and 2010–2015 by CA models with distance decay neighborhood to reveal the advantages and characteristics of modeling the neighborhood decay effect. The results show that the simulation accuracy of CA models with the spatial heterogeneous neighborhood and the hybrid neighborhood (HYN) is higher than that of CA models with homogeneous neighborhood, and in most cases, the HYN can more accurately describe the neighborhood decay effect.

The Recommendation of the Rural Ecological Civilization Pattern Based on Geographic Data Argumentation

For any rural area, a suitable ecological civilization model is of great significance and must be recommended taking into account its natural, social, and cultural characteristics so that the model is conducive to the sustainable development of its economy, environment, and industrial structure. However, the rural attribute data required for such a recommendation are often missing, and the data sparsity leads to the low accuracy of and poor training effect issues in recommendation algorithms. To address this issue, this paper proposes a geographic data augmentation method, namely the spatial factor on generative adversarial networks (S-GANs), which combines the generative adversarial network (GAN) with the Third Law of Geography. Specifically, the GAN is used to generate data for the rural ecological civilization recommender system, while the Third Law of Geography is used to ensure that the generated data conform to the real geographical environment. To test the effectiveness of the S-GAN method, the experiment used the enhanced rural attribute data as the input of three recommendation systems: RippleNet, KGCN, and KGNN-LS. Compared with the data before argumentation, the recommendation accuracy increased by 55.49%, 25.12%, and 27.14% in RippleNet, KGCN, and KGNN-LS, respectively. The experimental results show that the S-GAN is effective in geographic data argumentation for recommendation and is expected to be widely used in other geographic data argumentation fields.

Geographic information science in the era of geospatial big data: A cyberspace perspective

Geographic information science in the era of geospatial big data: A cyberspace perspective

Mapping crop leaf area index at the parcel level via inverting a radiative transfer model under spatiotemporal constraints: A case study on sugarcane

Crop leaf area index (LAI) mapping from remote sensing observations is highly demanded for regional agricultural applications, such as crop health monitoring and crop yield prediction. However, the popularly used model inversion method inevitably presents an ill-posed problem, which leads to unstable and inaccurate retrieval results. An agricultural parcel is a relatively homogeneous object due to uniform agricultural practices and similar environmental conditions. Crops inside a parcel generally present the same growth stage and similar growth status. In this study, a new method was proposed by adopting spatiotemporal constraints at the parcel level in the model inversion process for LAI retrieval. Firstly, phenology information of parcels was utilized to constrain the LAI ranges based on the established prior knowledge, which described the temporal variation of LAI during the life cycle of crop. Subsequently, spatial constraint was adopted in the model inversion through a proposed novel cost function, which assumed the spatial autocorrelation of parameters inside a parcel according to the first law of geography. Sugarcane was taken as an example to evaluate the proposed method. The method was applied to Sentinel-2 data and validated using ground-measured LAI data. The retrieved model parameters exhibited smoother spatial patterns and lower intra-parcel spatial variations through the proposed parcel-level inversion method, compared to the conventional pixel-level inversion method. Evaluations of LAI retrieval accuracy showed that the parcel-level inversion method yielded more accurate results (root mean square error (RMSE): 0.34 m2/m2; relative root mean square error (RRMSE): 20.89%), compared to the pixel-level inversion method (RMSE: 0.56 m2/m2; RRMSE: 34.53%). The spatiotemporal constraint strategy presented the ability to prevent severe overestimation and underestimation. Among the validation data set, the accuracy of the severely overestimated samples of the pixel-level method (RMSE: 0.93 m2/m2) was improved via the new method (RMSE: 0.43 m2/m2); the accuracy of the severely underestimated samples of the pixel-level method (RMSE: 0.66 m2/m2) was improved via the new method (RMSE: 0.26 m2/m2). Finally, the proposed method was applied to obtain sugarcane LAI mapping. The study demonstrates that the proposed parcel-level spatiotemporal constraint strategy improves the accuracy of LAI retrieval, has the capability of producing a highly reliable crop LAI mapping, and shows good potential for agricultural applications at regional scales.

Geographic Approach: Identifying Relatively Stable Tibetan Dialect and Subdialect Area Boundaries

Updating dialect maps requires extensive language surveys. Geographic methods can be applied to identify relatively stable boundaries of dialect and subdialect areas, allowing language surveys to focus on boundaries that may change and thereby reduce survey costs. Certain scholars have pointed out that the watershed boundary can be employed as the boundary of Tibetan dialect areas. This paper adds that the lowest-grade road breakpoint line and no-man’s-land boundary can also be used as essential indicators for determining stable (sub)dialect area boundaries. Combined with the revised First Law of Geography and the method of superposition analysis of geographic elements, this study identifies indicators that affect the stability of the Tibetan (sub)dialect area boundaries and evaluates the stability of each boundary segment. Due to the particularity of the study area, most Qinghai–Tibet Plateau (Chinese part) (sub)dialect area boundaries are stable. In addition, boundary inaccuracies caused by defects in the distribution of language survey samples can be identified by geographic approaches.

Modeling of Time Geographical Kernel Density Function under Network Constraints

Time geography considers that the probability of moving objects distributed in an accessible transportation network is not always uniform, and therefore the probability density function applied to quantitative time geography analysis needs to consider the actual network constraints. Existing methods construct a kernel density function under network constraints based on the principle of least effort and consider that each point of the shortest path between anchor points has the same density value. This, however, ignores the attenuation effect with the distance to the anchor point according to the first law of geography. For this reason, this article studies the kernel function framework based on the unity of the principle of least effort and the first law of geography, and it establishes a mechanism for fusing the extended traditional model with the attenuation model with the distance to the anchor point, thereby forming a kernel density function of time geography under network constraints that can approximate the theoretical prototype of the Brownian bridge and providing a theoretical basis for reducing the uncertainty of the density estimation of the transportation network space. Finally, the empirical comparison with taxi trajectory data shows that the proposed model is effective.

Integrating fuzzy set function into floating catchment area measures: a determination of spatial accessibility of service points

ABSTRACT The use of discrete (binary or zonal/step-wise) instead of continuous travel impedance functions in floating catchment area (FCA) measures generates inconsistent and less reliable accessibility indices that might be overestimating or underestimating actual accessibility. Thus, this study illustrates the limitations of discrete travel impedance function in the enhanced two-step (E2S) FCA method; and develops a fuzzy-based continuous travel impedance function by combining the degree to which travel distances belong to different sub-zones of catchment areas and their respective E2SFCA zonal weights using the weighted mean fuzzy-set operation. With the developed fuzzy-based travel impedance function, a new accessibility measure, fuzzy-based two-step (F2S) FCA is defined. The E2SFCA and F2SFCA measures were implemented in the hypothetical set up and Rural Wards of Dodoma Urban District in Tanzania to determine spatial accessibility of service points and water points, respectively. The resulting E2SFCA and F2SFCA accessibility indices portrayed a slightly similar pattern. However, F2SFCA accessibility indices varied among households within and across sub-zones, while E2SFCA accessibility indices were identical within but changed abruptly across sub-zones. The variations in F2SFCA accessibility indices reflect the reality and to a greater extent Tobler’s first law of Geography. Thus, the developed F2SFCA measure could be used to measure spatial accessibility of other services or commodities as it properly models travel impedance. The F2SFCA measure could further be improved to generate much more realistic accessibility indices by capturing local instead of total potential demand, commonly modelled in FCA measures.

How is the Third Law of Geography different?

ABSTRACT Three overarching principles governing patterns of geographic phenomena have been proposed that have been referred to by some as ‘laws of geography’. The first and the second principles address the spatial proximity and spatial heterogeneity of geographic phenomena. These principles, while powerful, fail to resonate with much geographical inquiry. The more recently proposed third principle concerns geographic similarity. The differences of it from the first two can be perceived in three basic aspects: principle expressed, form of expression and role of geographic examples (samples). The third principle emphasizes the geographic context of geographic variables in the form of geographic configuration, compared to a single spatial dimension that are emphasized in the first two principles. The third principle focuses on the comparative nature in the geographic configuration in terms of similarity, that is, in the form of ‘similar to’, as opposed to the relationships ‘between’ that are key to the first and second principles. The third principle emphasizes the individual representation of geographic examples, as opposed to the global representation of geographic examples. These differences not only distinguish the third principle as an important addition to the other two, but also provide a potentially transformative way to address the rigid requirements on samples in geographic analysis, particularly during this age when the collection and provision of geographic data are crowd-sourced and VGI-based. These differences also point to the potential of the third principle opening up a space of inquiry that would resonate more successfully with place-based approaches in human geography.

The Moran Spectrum as a Geoinformatic Tupu: implications for the First Law of Geography

ABSTRACT Geoinformatic Tupu, or Geoinformatic graph spectrum, is a theoretical as well as a technical framework for generalizing geographic knowledge and solving real world problems. Geoinformatic Tupu is a promising platform for capitalizing on the technical advances of Geographic Information Systems, and to integrate the Chinese traditional way of thinking with modern information technology. It has been one of the major research topics in the Chinese GIScience community in recent decades, with an evolving epistemological development. A core objective of Geoinformatic Tupu is to recover and represent geographic principles with the Tupu approach, which is adopted in this paper to formulate the First Law of Geography (FLG) [i.e. the law of spatial autocorrelation] as the Moran Spectrum – a combination of sequential diagrams, graphs, and numeric components. Using the Moran Spectrum as a conduit, we present the theory of Moran Eigenvector Spatial Filtering (MESF), a distinct branch of spatial statistics that has demonstrable advantages in statistical modelling and machine learning, but has yet to be widely disseminated due to its conceptual and computational complexity. This paper demonstrates the effectiveness of the Tupu approach in enriching the representation of the FLG as well as deepening its applications. It also suggests inclusion of the Moran Spectrum as a core component in Geoinformatic Tupu.

Connection Characteristics and Hierarchical Structure of China’s Urban Network‐Based on the Communications Technology Service Industry

Considering the importance of China’s digital economy, industrial Internet, and high‐quality development, this study analyzed China’s urban network from the perspective of the communications technology service industry. Three sub‐networks (R & D, sales, and investment) and a comprehensive network were constructed. The density, centrality, and cohesive subgroups of the above network were identified. The results show that: (1) cohesion of urban networks in China is weak and resource sharing is low. (2) From west to east, the urban network forms a multilevel diamond structure in the periphery, a parallelogram structure in the semiperiphery, and a triangle structure in the center. (3) The spatial distribution of cohesive subgroups is scattered, disobeying the first law of geography. By constructing sub‐networks and a comprehensive network, the subnetworks that dominate China’s urban networks were identified and their typical characteristics described. This study clarifies the technical support pattern behind China’s digital economy development and industrial internet construction and provides a basis for policy‐makers to optimize the country’s high‐quality development in the future.

A novel quality control model of rainfall estimation with videos – A survey based on multi-surveillance cameras

The widespread use of surveillance cameras has become an emerging means for rainfall observations. With the advantages of high spatial–temporal resolution, rainfall information obtained from surveillance videos is highly suitable for meteorological-related research and has bright prospects. However, due to the complex and variable monitoring scenarios, the quality of the rainfall data estimated by each camera is always inconsistent, resulting in low practical value. Dense ground-level surveillance cameras have temporal and spatial correlations that can be used to improve the accuracy of rainfall estimation through mutual verification. In this study, we first introduce camera parameters to refine the spatial volume of rainfall (SVoR)11Most acronyms used in this study appear in the Appendix 1. perceived by cameras to improve the accuracy of rainfall intensity (RI) estimation. Next, a novel quality control (QC) model of rainfall estimation with multi-surveillance camera collaboration that takes the rainfall observations of all cameras as input is proposed. (i) We build a reliability evaluation (RE) model for the estimation of RI in accordance with raindrop imagery features to provide a reference for the subsequent correction of RI estimation; (ii) inspired by the first law of geography (Tobler, 1970), we then construct a spatial–temporal consistency filter and a situation consistency filter by using the spatial–temporal constraints between cameras to coarsely evaluate the RI values; and (iii) the correlation between cameras is calculated based on the fuzzy method to further build a correlation filter for the fine-grained correction of RI values. Experiments show that our method can effectively eliminate RI outliers and improve the accuracy and reliability of rainfall estimation results. Moreover, our method is highly suitable for heavy and violent rainfall application scenarios and can provide high-resolution rainfall data support for flooding warnings and simulations in urban areas.

The Spatial Effect of Aging Population on Innovation in Entrepreneurship--Based on Empirical Analysis of Western China

This study researches the impact of an aging population on Innovation in Entrepreneurship (IE), and applying fixed effects models (FE), mediated effects models and spatial lag regression model (SAR) to panel data of Western China (excluding Tibet) from 2004 to 2019.The results showed that an aging population and IE inverted significantly U-curve, and human capital plays a significant partial mediation between the two. A theoretical perspective based on the First Law of Geography, in the western China, aging population and IE are both positive spatial correlation, and both show the characteristics of "High-High" spatial agglomeration. Under the spatial model, aging population and IE also inverted U-curve.

Geographic Graph Network for Robust Inversion of Particulate Matters

Although remote sensors have been increasingly providing dense data and deriving reanalysis data for inversion of particulate matters, the use of these data is considerably limited by the ground monitoring samples and conventional machine learning models. As regional criteria air pollutants, particulate matters present a strong spatial correlation of long range. Conventional machine learning cannot or can only model such spatial pattern in a limited way. Here, we propose a method of a geographic graph hybrid network to encode a spatial neighborhood feature to make robust estimation of coarse and fine particulate matters (PM10 and PM2.5). Based on Tobler’s First Law of Geography and graph convolutions, we constructed the architecture of a geographic graph hybrid network, in which full residual deep layers were connected with graph convolutions to reduce over-smoothing, subject to the PM10–PM2.5 relationship constraint. In the site-based independent test in mainland China (2015–2018), our method achieved much better generalization than typical state-of-the-art methods (improvement in R2: 8–78%, decrease in RMSE: 14–48%). This study shows that the proposed method can encode the neighborhood information and can make an important contribution to improvement in generalization and extrapolation of geo-features with strong spatial correlation, such as PM2.5 and PM10.

Geospatial Least Squares Support Vector Regression Fused with Spatial Weight Matrix

Due to the increasingly complex objects and massive information involved in spatial statistics analysis, least squares support vector regression (LS-SVR) with a good stability and high calculation speed is widely applied in regression problems of geospatial objects. According to Tobler’s First Law of Geography, near things are more related than distant things. However, very few studies have focused on the spatial dependence between geospatial objects via SVR. To comprehensively consider the spatial and attribute characteristics of geospatial objects, a geospatial LS-SVR model for geospatial data regression prediction is proposed in this paper. The 0–1 type and numeric-type spatial weight matrices are introduced as dependence measures between geospatial objects and fused into a single regression function of the LS-SVR model. Comparisons of the results obtained with the proposed and conventional models and other traditional models indicate that fusion of the spatial weight matrix can improve the prediction accuracy. The proposed model is more suitable for geospatial data regression prediction and enhances the ability of geospatial phenomena to explain geospatial data.

SPATIAL AUTOCORRELATION ANALYSIS OF HOUSING DISTRIBUTION IN JOHOR BAHRU

Geographic location naturally generates spatial patterns that are either clustered, dispersed, or random. Moreover, Tobler’s First Law of Geography is essentially a testable assumption in the concept where geographic location matters and one method for quantifying Tobler’s law of geography is through measures of spatial autocorrelation. Therefore, the purpose of this study is to identify the spatial patterns of housing distribution in Johor Bahru through the spatial autocorrelation method. The result of the global spatial autocorrelation analysis demonstrates a high degree of clustering within the housing distribution, as well as the identification of a clustered pattern with a highly positive Moran’s I value of 0.995207. Following that, the LISA cluster map successfully identified individual clusters of each housing unit with their neighbours through the red and blue colours displayed on the map, as well as revealing home buyers’ preferences for a property in each location.

GIScience and remote sensing in natural resource and environmental research: Status quo and future perspectives

Geographic information science (GIScience) and remote sensing have long provided essential data and methodological support for natural resource challenges and environmental problems research. With increasing advances in information technology, natural resource and environmental science research faces the dual challenges of data and computational intensiveness. Therefore, the role of remote sensing and GIScience in the fields of natural resources and environmental science in this new information era is a key concern of researchers. This study clarifies the definition and frameworks of these two disciplines and discusses their role in natural resource and environmental research. GIScience is the discipline that studies the abstract and formal expressions of the basic concepts and laws of geography, and its research framework mainly consists of geo-modeling, geo-analysis, and geo-computation. Remote sensing is a comprehensive technology that deals with the mechanisms of human effects on the natural ecological environment system by observing the earth surface system. Its main areas include sensors and platforms, information processing and interpretation, and natural resource and environmental applications. GIScience and remote sensing provide data and methodological support for resource and environmental science research. They play essential roles in promoting the development of resource and environmental science and other related technologies. This paper provides forecasts of ten future directions for GIScience and eight future directions for remote sensing, which aim to solve issues related to natural resources and the environment.

A Weight Assignment Algorithm for Incomplete Traffic Information Road Based on Fuzzy Random Forest Method

One of the keys in time-dependent routing is determining the weight for each road network link based on symmetrical and complete traffic information. To facilitate travel planning considering traffic situations based on historical global position system (GPS) trajectory data which uncover the whole road network, this paper proposes a fuzzy random forest-based road section data estimation method, which uses the third law of geography as the core idea. For different time periods, road grade, tidal lane, proximity to infrastructure (main places that affect traffic, such as schools, hospitals), and accident road sections were selected as indicators that influence the traffic. The random forest algorithm is used to build the mapping relationship between attribute data with average traffic which is obtained based on GPS data. Subsequently, the fuzzy reasoning method is used to obtain the weight of road links missing traffic information by calculating their similarities with typical road section samples. Using the road network of Suzhou City as an example, the proposed method was used to analyze estimate the average driving speeds of road sections with missing traffic information for different time periods. Experimental results show that this method can effectively avoid congested road sections and obtain high-speed travel routes.