Visualizing spatial interaction characteristics with direction-based pattern maps

This study aims at developing a data‐driven and bottom‐up spatial statistic method for identifying regions of anomalous spatial interactions (clusters of extremely high‐ or low‐value spatial flows), based on which it creates a spatial flow weights matrix. The method, dubbed flowAMOEBA, upgrades a multidirectional optimum ecotope‐based algorithm (AMOEBA) from areal data to spatial flow data through a proper spatial flow neighborhood definition. The method has the potential to dramatically change the way we study spatial interactions. First, it breaks the convention that spatial interaction data are always collected and modeled between spatial entities of the same granularity, as it delineates the OD region of anomalous spatial interactions, regardless of the size, shape, scale, or administrative level. Second, the method creates an empirical spatial flow weights matrix that can handle network autocorrelation embedded in spatial interaction modeling, thus improving related policy‐making or problem‐solving strategies. flowAMOEBA is tested and demonstrated on a synthetic data set as well as a county‐to‐county migration data set.

Spatial scale is a fundamental issue for geographical phenomena because the size of the spatial unit adopted for analysis can have a significant effect on aggregated spatial data and the corresponding analytical results. There exists much research on the scale issue of spatial distribution data while a few has paid attention to the scale effect on spatial interactions. Big geo-data with micro-level individual movements provide an unprecedented opportunity to explore spatial interactions from the bottom up and to understand the scale effect from the perspective of interaction patterns. In this paper, we introduced an empirical framework to explore spatial interaction data across scales. By incorporating two data sets of taxi trajectories in Beijing and Shanghai, we aggregated the taxi O-D trips under multiple regular grids with different cell sizes and analyzed the properties of spatial interactions in three aspects: the statistical distribution, the distance decay mechanism, and the structure pattern. Our research demonstrated the feasibility of analyzing spatial interactions from a multi-scale view and offered a basic empirical framework for future geographical research that is interested in scale and spatial interactions.

Definition:Spatial interaction refers to all cases in which a relationship (a flow of persons, goods, communications, citations, and so on) is observed between pairs of spatial units. Differently from the standard areal or point units often analyzed in spatial science, spatial interaction data are denoted by their bilateral nature. They have an origin and a destination (in the particular case, the two being the same). Such origins and destinations may have observable characteristics. Moreover, each pair of spatial units may be described according to some measures of separation (such as distance) or of commonality (e.g., the presence of a common language). Such descriptors are typically employed in spatial interaction models, the most commonly used analytical framework for modeling flow data. Problems occur when the aforementioned descriptors exhibit correlation between the values of different spatial units or dyads which is due to the spatial configuration of the units themselves (e.g., ...This is an excerpt from the content.

New and expanding sources of spatial big data hold tremendous potential for regional policy analysis. Such data enable us to analyze regional policies in ways not possible with traditional sources of data, such as administrative records. At the same time, the use of spatial big data is fraught with issues and challenges that must be addressed. In this paper, we discuss both the opportunities and challenges of using spatial big data for regional policy analysis. We also explore analytical issues tied to the use of regional policy analysis methods in the era of big data, as well as the state of art in applying such methods to spatial big data. Our discussion focusses on three types of methods: (1) statistical and regression modeling, (2) traditional nonparametric modeling, and (3) deep neural learning.

This paper addresses the theme of spatial autocorrelation impacting spatial equilibria, and hence an understanding of economic network resilience and vulnerability. It exploits the notion that spatial autocorrelation in the geographic distribution of origin and destination attributes and network autocorrelation in the flows between origins and destinations constitute two spatial autocorrelation components contained in spatial interaction data. It illustrates that a spatial interaction model specification needs to incorporate both components in order to furnish sound implications about associated economic network resilience and vulnerability. Such models also need to undergo sensitivity analyses in terms of changes in geographic scale and resolution. And, it furnishes a novel 3-D visualization of geographic flows, such as journey-to-work trips, in order to achieve a better comprehension of economic network resilience and vulnerability.

The structure and use of urban land have a key role in a transportation system has significant impact on Batang land use. Because it is very influential on the demand and capacity of the transportation system related to human mobility. The relationship between land use and transportation systems can be defined in terms of spatial interaction. This spatial interaction arises because of the generation and attraction activity of the movement between two important entities. The higher the activity, the stronger the interaction will be. In addition, the form of urban spatial planning also greatly influences the interactions that occur. However, the problem is that if there is a large infrastructure project, it will tend to trigger changes in land use along with the spatial interactions in it. This article will examine in more depth the possibility of spatial interactions that occur in the Batang Regency due to the activities of the Batang Integrated Industrial Estate. So that it can be seen the dynamics of land-use changes that occur due to large spatial interactions in the area. The approach used in this research is quantitative deductive. The discussion in this article consists of several stages, namely a review of literature studies on land use and spatial interactions. Furthermore, the results of the review carried out the process of modeling the spatial interaction of transportation to describe the activities that occur in the area. The results show that the spatial interaction of transportation will be oriented along the main road axis and has a high intensity. Because the main road tends to be served efficiently by public transportation. However, in an area that is not traversed by the main road axis, spatial interactions still exist with random patterns and low intensity.

Metropolitan Urban Mamminasata South Sulawesi, Indonesia as the object of study is explored in the core-peripheral spatial interaction towards the formation of suburban service centers. The problems raised in this study are (i) is there a relationship/correlation between spatial integration, spatial interaction, and urban agglomeration to the formation of urban activity systems and (ii) how the formation of urban activity systems works as a determinant of economic growth, land use change and environmental quality degradation towards sustainable development in the metropolitan city of Mamminasata. Comparative studies of suburban areas have been carried out over three time periods (2001, 2015 and 2019). Data elaboration on observations, surveys and documentation is done to describe urban dynamics in terms of economic, social and environmental aspects. Path analysis is used to address direct effects, indirect effects, differences, and dependencies between urban elements. The gravity model is used to analyze the spatial interactions of the core city with the periphery. The study results show that spatial integration, spatial interaction and urban agglomeration have a positive effect on the system of urban activity and economic growth in the outskirts of the Mamminasata Metropolitan area. The results of this study recommend policy makers and urban planners that land use change, spatial integration and urban spatial interactions on the spatial scale of metropolitan cities to require the implementation of sustainable development concepts oriented towards saving the environment, ensuring fairness in economic access and creating social cohesion, in line with meeting national Metropolitan city development targets by 2030.

Taking a byte out of big data

Multi-scale telecoupling effects of land use change on ecosystem services in urban agglomerations --A case study in the middle reaches of Yangtze River urban agglomerations

Relationships between land use and water quality of rivers and lakes vary spatially and temporally. These variations were analyzed using spatial analysis and mathematical statistical methods for the Suzhou Creek in Shanghai. Based on the data of water quality and land use in 2001， 2005， 2010， 2015， and 2020， five spatial scales （200， 500， 1 000， 2 000， and 5 000 m reach buffer） of the landscape pattern were extracted using correlation and redundancy analysis to explore the impact of land use composition and spatial pattern on water quality at different spatial and temporal scales. The results showed that： ① the water quality of Suzhou Creek has gradually improved in the past 20 years； other indicators were between Class II to Class IV in 2020 except TN， and TN was the main pollutant. ② The main land use type of the buffer zone was construction land， and the proportion of greenland and woodland showed a small growth trend. ③ The water quality was closely related to landscape pattern， showing temporal and spatial scale effects. On the time scale， indicators such as construction land， agricultural land， landscape dominance， aggregation， and diversity had significant correlations with various water quality parameters， and there was an inverse correlation in 2010 compared with that in other years for NH4+-N， TP， and TN. The landscape pattern in 2001 had the greatest explanation for water quality， with an explanation rate of 93.65%. The impact of greenland and woodland on water quality has begun to emerge in the past 10 years. ④ On the spatial scale， there were significant correlations between greenland and woodland， patch number， landscape shape index， diversity index， and water quality. There was a strong positive regulatory effect of greenland and woodland on NH4+-N， TP， and TN at the scale of 2 000 m. The patch number and landscape shape index had relatively strong regulatory effects on water quality on a larger spatial scale， whereas the Shannon diversity index had a better positive regulatory effect on water quality on a small scale. The landscape pattern within a buffer of 2 000 m had the highest interpretation degree for all factors， with an explanation rate of 68.47%. The study showed that rationally planning the proportion of greenland and woodland within the 2 000 m buffer zone and optimizing its landscape configuration is an important measure to purify the surface water quality of Suzhou Creek.

Spatial interaction is an important phenomenon that reflects the human–land relationship and has long been a core topic in multiple fields, such as urban planning, transportation planning, commodity trade, and epidemic prevention. However, as an underlying cause of spatial interaction, function complementarity has been ignored by existing research for a long time. At the same time, the increase in Big Data of travel behavior provides an opportunity to model spatial interactions in detail. In this paper, we proposed three types of land-use function complementarity indices according to the spatiotemporal characteristics of human mobility. These complementarity indices are introduced to spatial interaction to improve the gravity model. We also examined the effects of land function complementarity on intra-urban spatial interaction using smart card records of metro system for different time periods and directions. The results showed that all models could be improved by introducing the land-use function complementarity indices, but the models with a single travel pattern and clear direction were explained more by the complementary indices. The indices we propose in this paper could be used for predicting spatial flow and trip distribution, and also could be considered as factors in researches about transportation and land-use planning.

Soil erosion by water is one of the most important land degradation processes in Mediterranean environments. This process is strongly linked to problems of flooding and channel management. This article reviews existing knowledge on these topics and defines research gaps. In the framework of environmental change studies it is important to consider soil erosion at various spatial and temporal scales. Most field measurements and modelling efforts have hitherto concentrated on water erosion processes operating at the runoff plot scale. Soil erosion processes operating at other spatial scales have received much less attention in the literature. Yet, there are indications that gully and channel erosion are probably the dominant sediment sources in a variety of Mediterranean environments. Beside water erosion, other erosion processes operating within catchments, such as tillage erosion, land reshaping for land preparation (e.g., terracing) or soil quarrying can have significant impacts on soil profile truncation. Land use changes strongly affect the intensity of these processes. The conditions, position and connectivity of the runoff and sediment generating areas within catchments have a profound effect on flood characteristics within the main channels but the dynamics are not well understood. Some research has taken place into meteorological conditions producing catastrophic flooding and into development of hydrological models using catchment variables. Much less is known of the properties and effects of flood waves within channels, partly because of lack of records of these infrequent events. It is not only water but also sediment which causes destruction in floods, yet sediment is frequently ignored in channel management. The extreme conditions associated with floods in the region, the variability of flows and of flood zones, the mobility of the channels and the high sediment loads create particular challenges for channel management. Trends in land use and channel management are tending to exacerbate these problems. From this review it can be concluded that there is still an important need for process-based understanding and modelling of key soil erosion processes operating at a range of scales: i.e., from plots over hillslopes, catchments to regions. In particular, more research is needed on the linkages between upland areas which produce large volumes of runoff and sediment and channels on the other hand. Such linkages are through gullies and sedimentation zones. Monitoring and experi mental data on key soil erosion and channel processes operating within Mediterranean landscapes are crucial for the improvement of soil erosion and channel models for a range of scales. In particular, long-term monitoring of soil erosion processes and stream channel changes seems to be essential to observe the effects of infrequent torrential rain events on severe erosion, flooding and stream channel changes as well as on the transient response of Mediterranean landscapes to changes in land use and climate. Systematic collation of historical evidence of changes would be valuable. Implications of land and water use need to be examined in detail. A wide range of alternative strategies and techniques of channel and basin management must be explored and modelled. A holistic approach to management of the fluvial system is recommended.

This paper proposes a set of specific crossover and mutation operators for the delineation of functional regions through evolutionary computation. We consider a problem of dividing a given territory into local labor market areas based on spatial interaction data. Such areas are defined so that a high degree of inter-regional separation and intra-regional integration - in both cases in terms of commuting flows - exist. A genetic algorithm has been designed based on the maximization of a fitness function that measures aggregate intra-region interaction under constraints of inter-region separation and minimum size. Additional requirements, typical of any functional regionalization, include the absence of overlapping between delineated regions and an exhaustive coverage of the whole territory (so all basic spatial units must be allocated to one and only one-region). The complex set of restrictions results in conventional operators often generating invalid solutions, impeding or delaying the evolutionary process. This is the reason why an extensive set of operators has been designed that incorporates knowledge about the problem, allowing the evolution of the set of solutions towards the final result.

A recent focus in the academic literature on segregation indices is on the development of spatial measures, which make use of information on the relative spatial locations of zones; such measures overcome the spatial location independence of traditional indices, often expressed as the ‘checkerboard problem’. Most spatial measures are based on contiguity weighting - immediate neighbours of a given zone are included in calculations - or distance-decay functions. This chapter considers the restrictions of such approaches and discusses some ways in which distance decay weighting schemes can be adapted to better reflect interactions between people in different areas. The use of data on internal migration as a proxy for the daily interactions between Catholics and Protestants in Northern Ireland is considered, and is shown to depict limited interactions between those on opposite sides of so-called ‘interface areas’. Comparison of results using geographically weighted distance-decay schemes suggests that simple distance-based approaches may, in some cases, have limited practical utility if the derived measures are to be used as a guide to the degree of mixing between members of different population groups.

Spatial statistics: A quantitative geographer’s perspective