Contiguous Clusters Research Articles

Can urban agglomeration policies promote regional economic agglomeration? Evidence from the Yangtze River Economic Belt in China

The impact of implementing urban agglomeration policies (UAPs) on regional economic agglomeration is crucial for achieving coordinated regional development. This study examines the effects of UAPs on economic agglomeration in the Yangtze River Economic Belt (YREB) in China using panel data from 108 cities spanning from 2006 to 2020. Various research methodologies, such as spatio-temporal variation analysis, Differences-in-Differences (DID), Propensity Score Matching DID (PSM-DID), and Spatial Autocorrelation DID (SACDID) models, are employed. The results indicate a diffusion effect in economic agglomeration of the YREB, leading to a transition from a multilevel center-periphery structure to a contiguous clustering pattern. UAPs have, however, resulted in the concentration of the regional economy towards central cities. Urban agglomerations exhibit an internal siphon effect and a diffusion effect on their exterior. The combination of these two effects manifests itself as a certain degree of siphon. Specifically, UAPs have caused both siphon and diffusion effects in the middle region of the YREB, with less significant impacts in the eastern and western regions. The siphon effects of central cities are influenced by both geographical distance from other cities and, more importantly, economic disparities between them. During the study period, the UAPs served as the indirect rather than direct factor of regional economic coordination.

Are intersectoral GDP contributions similar with nearby states? A semi-model-based spatial cluster analysis

ABSTRACT Intersectoral Gross Domestic Product (GDP) contributions reflect economic development in different industries. A good understanding of clusters of intersectoral GDP contributions among different subregions plays a vital role in making local policies and building economic development strategies. We propose a semi-model-based clustering method, i.e, a Markov random field constraint mixture of finite mixtures model to tackle this issue. Our proposed method has the advantage of 1) data-driven determination of the final number of clusters and 2) allowing for both locally spatially contiguous clusters and globally discontiguous clusters. Posterior inference is performed with an efficient Markov chain Monte Carlo (MCMC) algorithm. We demonstrate the performance of the proposed method using both simulation studies and a real-world example where intersectoral GDP contribution of year 2019 data, obtained from the U.S. Bureau of Economic Analysis, is studied.

Hespdiv: an R package for spatially constrained, hierarchical and contiguous regionalization in palaeobiogeography

AbstractThe objective determination of boundaries of bioregions is a nontrivial problem. Here we present a new method family, HespDiv, and an algorithm for its implementation in a new R package: hespdiv. The hespdiv algorithm performs iterative hierarchical nonlinear spatial subdivisions of taxic data into topologically contiguous geographic regions. Bioregions obtained in this way can be viewed as realistic and causally important entities belonging to eco‐genealogical Bretskyan hierarchy. The possibilities of the hespdiv algorithm are successfully demonstrated on the Miocene mammal data from the contiguous United States, where a hierarchical set of bioregions was determined. The algorithm works in a decision‐tree‐like manner by generating multiple split‐lines, that each subdivide a study area and data into two parts per iteration per region. The performance of each split‐line is measured using a combination of data generalization and comparison functions that can be custom made or selected from pre‐set subdivision methods. In each iteration, the best split‐line is used to produce a subdivision, until no more adequate split‐lines are found. The process results in a hierarchy of subdivisions of distribution of taxonomic occurrences in space. Benefits of hespdiv include the delineation of spatially contiguous clusters, flexibility, and unique representations of spatial hierarchy tree. The package is shown to be effective in determining significant contiguous spatial structures of biota, so called geobiomes, in sufficiently sampled time intervals and regions. The flexibility and ease to use of the approach allows its application to the whole range of palaeo‐variables and environmental proxies.

Saccadic targeting in the Landolt-C task: Implications for Chinese reading.

Participants in an eye-movement experiment performed a modified version of the Landolt-C paradigm (Williams & Pollatsek, 2007) to determine if there are preferred viewing locations when they searched for target squares embedded in linear arrays of spatially contiguous clusters of squares (i.e., sequences of one to four squares having missing segments of variable size and orientation). The results of this experiment indicate that, although the peaks of the single- and first-of-multiple-fixation landing-site distributions were respectively located near the centers and beginnings of the clusters, thereby replicating previous patterns that have been interpreted as evidence for the default saccadic-targeting hypothesis, the same dissociation was evident on nonclusters (i.e., arbitrarily defined regions of analysis). Furthermore, properties of the clusters (e.g., character number and gap size) influenced fixation durations and forward saccade length, suggesting that ongoing stimulus processing affects decisions about when and where (i.e., how far) to move the eyes. Finally, results of simulations using simple oculomotor-based, default-targeting, and dynamic-adjustment models indicated that the latter performed better than the other two, suggesting that the dynamic-adjustment strategy likely reflects the basic perceptual and motor constraints shared by a variety of visual tasks, rather than being specific to Chinese reading. The theoretical implications of these results for existing and future accounts of eye-movement control are discussed. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Bayesian spatial cluster signal learning with application to adverse event (AE)

ABSTRACT There is growing interest in understanding geographic patterns of medical device-related adverse events (AEs). A spatial scan method combined with the likelihood ratio test (LRT) for spatial-cluster signal detection over the geographical region is universally used. The spatial scan method used a moving window to scan the entire study region and collected some candidate sub-regions from which the spatial-cluster signal(s) will be found. However, it has some challenges, especially in computation. First, the computational cost increased when the number of sub-regions increased. Second, the computational cost may increase if a large spatial-cluster pattern is present and a flexible-shaped window is used. To reduce the computational cost, we propose a Bayesian nonparametric method that combines the ideas of Markov random field (MRF) to leverage geographical information to find potential signal clusters. Then, the LRT is applied for the detection of spatial cluster signals. The proposed method provides an ability to capture both locally spatially contiguous clusters and globally discontiguous clusters, and is manifested to be effective and tractable using hypothetical Left Ventricular Assist Device (LVAD) data as an illustration.

Joint Random Partition Models for Multivariate Change Point Analysis

Change point analyses are concerned with identifying positions of an ordered stochastic process that undergo abrupt local changes of some underlying distribution. When multiple processes are observed, it is often the case that information regarding the change point positions is shared across the different processes. This work describes a method that takes advantage of this type of information. Since the number and position of change points can be described through a partition with contiguous clusters, our approach develops a joint model for these types of partitions. We describe computational strategies associated with our approach and illustrate improved performance in detecting change points through a small simulation study. We then apply our method to a financial data set of emerging markets in Latin America and highlight interesting insights discovered due to the correlation between change point locations among these economies.

Cis-regulatory modes of Ultrabithorax inactivation in butterfly forewings.

Hox gene clusters encode transcription factors that drive regional specialization during animal development: for example the Hox factor Ubx is expressed in the insect metathoracic (T3) wing appendages and differentiates them from T2 mesothoracic identities. Hox transcriptional regulation requires silencing activities that prevent spurious activation and regulatory crosstalks in the wrong tissues, but this has seldom been studied in insects other than Drosophila, which shows a derived Hox dislocation into two genomic clusters that disjoined Antennapedia (Antp) and Ultrabithorax (Ubx). Here, we investigated how Ubx is restricted to the hindwing in butterflies, amidst a contiguous Hox cluster. By analysing Hi-C and ATAC-seq data in the butterfly Junonia coenia, we show that a Topologically Associated Domain (TAD) maintains a hindwing-enriched profile of chromatin opening around Ubx. This TAD is bordered by a Boundary Element (BE) that separates it from a region of joined wing activity around the Antp locus. CRISPR mutational perturbation of this BE releases ectopic Ubx expression in forewings, inducing homeotic clones with hindwing identities. Further mutational interrogation of two non-coding RNA encoding regions and one putative cis-regulatory module within the Ubx TAD cause rare homeotic transformations in both directions, indicating the presence of both activating and repressing chromatin features. We also describe a series of spontaneous forewing homeotic phenotypes obtained in Heliconius butterflies, and discuss their possible mutational basis. By leveraging the extensive wing specialization found in butterflies, our initial exploration of Ubx regulation demonstrates the existence of silencing and insulating sequences that prevent its spurious expression in forewings.

Cis-regulatory modes of Ultrabithorax inactivation in butterfly forewings

Hox gene clusters encode transcription factors that drive regional specialization during animal development: for example the Hox factor Ubx is expressed in the insect metathoracic (T3) wing appendages and differentiates them from T2 mesothoracic identities. Hox transcriptional regulation requires silencing activities that prevent spurious activation and regulatory crosstalks in the wrong tissues, but this has seldom been studied in insects other than Drosophila, which shows a derived Hox dislocation into two genomic clusters that disjoined Antennapedia (Antp) and Ultrabithorax (Ubx). Here, we investigated how Ubx is restricted to the hindwing in butterflies, amidst a contiguous Hox cluster. By analysing Hi-C and ATAC-seq data in the butterfly Junonia coenia, we show that a Topologically Associated Domain (TAD) maintains a hindwing-enriched profile of chromatin opening around Ubx. This TAD is bordered by a Boundary Element (BE) that separates it from a region of joined wing activity around the Antp locus. CRISPR mutational perturbation of this BE releases ectopic Ubx expression in forewings, inducing homeotic clones with hindwing identities. Further mutational interrogation of two non-coding RNA encoding regions and one putative cis-regulatory module within the Ubx TAD cause rare homeotic transformations in both directions, indicating the presence of both activating and repressing chromatin features. We also describe a series of spontaneous forewing homeotic phenotypes obtained in Heliconius butterflies, and discuss their possible mutational basis. By leveraging the extensive wing specialization found in butterflies, our initial exploration of Ubx regulation demonstrates the existence of silencing and insulating sequences that prevent its spurious expression in forewings.

The star formation history of the Sco-Cen association

We reconstructed the star formation history of the Sco-Cen OB association using a novel high-resolution age map of the region. We developed an approach to produce robust ages for Sco-Cen’s recently identified 37 stellar clusters using the SigMA algorithm. The Sco-Cen star formation timeline reveals four periods of enhanced star formation activity, or bursts, remarkably separated by about 5 Myr. Of these, the second burst, which occurred about 15 million years ago, is by far the dominant one, and most of Sco-Cen’s stars and clusters were in place by the end of this burst. The formation of stars and clusters in Sco-Cen is correlated but not linearly, implying that more stars were formed per cluster during the peak of the star formation rate. Most of the clusters that are large enough to have supernova precursors were formed during the second burst around 15 Myr ago. Star and cluster formation activity has been continuously declining since then. We have clear evidence that Sco-Cen formed from the inside out and that it contains 100-pc long chains of contiguous clusters exhibiting well-defined age gradients, from massive older clusters to smaller young clusters. These observables suggest an important role for feedback in forming about half of Sco-Cen stars, although follow-up work is needed to quantify this statement. Finally, we confirm that the Upper-Sco age controversy discussed in the literature during the last decades is solved: the nine clusters previously lumped together as Upper-Sco, a benchmark region for planet formation studies, exhibit a wide range of ages from 3 to 19 Myr.

Quantifying the spatial clustering characteristics of radiographic emphysema explains variability in pulmonary function

Quantitative assessment of emphysema in CT scans has mostly focused on calculating the percentage of lung tissue that is deemed abnormal based on a density thresholding strategy. However, this overall measure of disease burden discards virtually all the spatial information encoded in the scan that is implicitly utilized in a visual assessment. This simplification is likely grouping heterogenous disease patterns and is potentially obscuring clinical phenotypes and variable disease outcomes. To overcome this, several methods that attempt to quantify heterogeneity in emphysema distribution have been proposed. Here, we compare three of those: one based on estimating a power law for the size distribution of contiguous emphysema clusters, a second that looks at the number of emphysema-to-emphysema voxel adjacencies, and a third that applies a parametric spatial point process model to the emphysema voxel locations. This was done using data from 587 individuals from Phase 1 of COPDGene that had an inspiratory CT scan and plasma protein abundance measurements. The associations between these imaging metrics and visual assessment with clinical measures (FEV_1, FEV_1-FVC ratio, etc.) and plasma protein biomarker levels were evaluated using a variety of regression models. Our results showed that a selection of spatial measures had the ability to discern heterogeneous patterns among CTs that had similar emphysema burdens. The most informative quantitative measure, average cluster size from the point process model, showed much stronger associations with nearly every clinical outcome examined than existing CT-derived emphysema metrics and visual assessment. Moreover, approximately 75% more plasma biomarkers were found to be associated with an emphysema heterogeneity phenotype when accounting for spatial clustering measures than when they were excluded.

An observer-blinded, cluster randomised trial of a typhoid conjugate vaccine in an urban South Indian cohort

BackgroundTyphoid fever causes nearly 110,000 deaths among 9.24 million cases globally and disproportionately affects developing countries. As a control measure in such regions, typhoid conjugate vaccines (TCVs) are recommended by the World Health Organization (WHO). We present here the protocol of a cluster randomised vaccine trial to assess the impact of introducing TyphiBEV® vaccine to those between 1 and 30 years of age in a high-burden setting.MethodsThe primary objective is to determine the relative and absolute rate reduction of symptomatic, blood-culture-confirmed S. Typhi infection among participants vaccinated with TyphiBEV® in vaccine clusters compared with the unvaccinated participants in non-vaccine clusters. The study population is residents of 30 wards of Vellore (a South Indian city) with participants between the ages of 1 and 30 years who provide informed consent. The wards will be divided into 60 contiguous clusters and 30 will be randomly selected for its participants to receive TyphiBEV® at the start of the study. No placebo/control is planned for the non-intervention clusters, which will receive the vaccine at the end of the trial. Participants will not be blinded to their intervention. Episodes of typhoid fever among participants will be captured via stimulated, passive fever surveillance in the area for 2 years after vaccination, which will include the most utilised healthcare facilities. Observers blinded to the participants’ intervention statuses will record illness details. Relative and absolute rate reductions will be calculated at the end of this surveillance and used to estimate vaccine effectiveness.DiscussionThe results from our trial will allow countries to make better-informed decisions regarding the TCV that they will roll-out and may improve the global supplies and affordability of the vaccines.Trial registrationClinical Trials Registry of India (CTRI) CTRI/2022/03/041314. Prospectively registered on 23 March 2022 (https://ctri.nic.in/Clinicaltrials/pmaindet2.php?trialid=62548&EncHid=&userName=vellore%20typhoid). CTRI collects the full WHO Trial Registration Data Set.

Spatially adaptive machine learning models for predicting water quality in Hong Kong

Water quality prediction in the spatially heterogeneous environment is challenging as the importance of water quality parameters (WQPs) and the performance of prediction models may vary across space. Thus, this study proposed spatially adaptive machine learning models to predict water quality status in Hong Kong. First, spatial clusters with relatively homogeneous water quality were adaptively detected using dynamically constrained agglomerative clustering and partitioning. Then, the optimal prediction models were constructed for each cluster by locally evaluating the prediction performance of six standalone machine learning models, including multi-layer perceptron neural network (MLPNN), support vector machine (SVM), random forest (RF), extremely randomized tree (ET), eXtreme gradient boosting (XGBoost) and categorical gradient boosting (CatBoost), as well as four novel hybrid models (MLPNN-SVM, ET-CatBoost, MLPNN-CatBoost and XGBoost-CatBoost). Finally, a sensitivity analysis was conducted to explore the minimum sets of indicative WQPs to achieve more cost-efficient water quality prediction based on locally optimal prediction models. The results revealed that the water quality in the study area was spatially heterogeneous and four spatially contiguous clusters were identified. MLPNN-SVM, ET-CatBoost, MLPNN-CatBoost and CaBboost performed best in Cluster 1 to Cluster 4, with R2 values of 0.917, 0.906, 0.901 and 0.937 and RMSE values of 1.978, 0.843, 2.020 and 1.572, respectively. The results of the sensitivity analysis indicated that acceptable local prediction results can be obtained using fewer WQPs. It is conducive to issuing timely water quality warnings and striving for more time for water pollution remediation.

Leaders or laggards in climate action? Assessing GHG trends and mitigation targets of global megacities

Urban areas account for between 71% and 76% of CO2 emissions from global final energy use and between 67–76% of global energy use. The highest emitting 100 urban areas (defined as contiguous population clusters) account for 18% of the global greenhouse gas (GHG) emissions. To date there is no comprehensive study of megacities (10 million+ population) analysing their historic population, economic and emission patterns and contributions to global GHGs. A key challenge is that a majority of these megacities (33 out of 41) are located in developing countries, making it challenging to track their rapidly mounting emissions. In this research, we capitalize on recently released open-access datasets—the Global Human Settlements Database (R2019A) and the World Urbanization Prospects (2018) for analyzing megacity development and GHG trends, vis-à-vis the mitigation targets outlined in their climate action plans. We find that as leading political and economic centres in their nations, though most megacities have initiated climate action plans, the aggregate impact of megacities on global emissions is limited. Based on this evidence, we explore how rapidly growing megacities can hedgehop to effectively reduce their GHG emissions while urbanizing and developing economically.

Numerical analysis of vortex-induced vibration on a flexible cantilever riser for deep-sea mining system

The flexible cantilever riser, as a special form of the marine riser, can be encountered in a deep-sea mining system, where the bottom of the long vertical lifting pipeline is connected with the intermediate warehouse. The main objective of this paper is to investigate the effects of the bottom weight caused by the intermediate warehouse and the flow speed on the dynamic responses of the cantilever pipeline. A quasi-3D coupling algorithm based on the discrete vortex method and finite element method is employed to calculate the unsteady hydrodynamic forces and vortex-induced vibrations of this pipeline in the time domain, respectively. We first simulate the VIV of a long flexible riser with two fixed ends in a stepped flow to validate the feasibility of the present method. Then, systematic simulations of cross-flow VIV of the cantilever riser are carried out under a wide range of bottom weights and different current speeds. The number of the vibration mode shows the decreasing tendency with the increase of the bottom weight. In a certain range of the weight, the number of the dominant mode remains unchanged, while the vibration amplitude declines with increasing weight. An amplitude jump phenomenon can be observed when the transition of the dominant mode in two contiguous mode clusters occurs. Moreover, the higher-order modes are excited with the increase of the current speed.

Spatial regionalization based on optimal information compression

Regionalization, spatially contiguous clustering, provides a means to reduce the effect of noise in sampled data and identify homogeneous areas for policy development among many other applications. Existing regionalization methods require user input such as the number of regions or a similarity measure between regions, which does not allow for the extraction of the natural regions defined solely by the data itself. Here we view the problem of regionalization as one of data compression and develop an efficient, parameter-free regionalization algorithm based on the minimum description length principle. We demonstrate that our method is capable of recovering planted spatial clusters in noisy synthetic data, and that it can meaningfully coarse-grain real demographic data. Using our description length formulation, we find that spatial ethnoracial data in U.S. metropolitan areas has become less compressible over the period from 1980 to 2010, reflecting the rising complexity of urban segregation patterns in these metros.

COMSOL-based investigation of the characteristics of microscopic water flooding and residual oil distribution in carbonate reservoirs

Carbonate reservoirs are rich in oil and gas reserves; thus, they have great exploitation potential. Therefore research on the microscopic mechanisms of carbonate reservoirs is of great significance. Based on the thin section images of core castings of typical Well 555 and its pore and fracture features in actual reservoirs, this study designed three kinds of images representing the pore and throat structure of real rocks by applying image stitching and binarization processing methods. A microscopic pore model of carbonate rocks was then established using COMSOL numerical simulation software. The microscopic water flooding characteristics and residual oil distributions of different schemes were observed by designing different fracture development forms. The fractures that developed in parallel main lines showed a more obvious influence on water flooding characteristics compared to fractures that developed in vertical main lines. The cluster residual oil was the main residual oil type in the early stage of water flooding in the pure matrix model. With the progress of water flooding, the continuous cluster residual oil gradually turned into mainly discontinuous porous and columnar residual oils. Vertical mainline fractures reduced the amount of residual oil in clusters and replaced it with columnar residual oil. In contrast, parallel main line fractures expanded the unswept area, with the residual oil appearing in contiguous clusters. This study microscopically analyzed the law and characteristics of water flooding in carbonate reservoirs to provide key theoretical support for enhancing oil recovery.

Geostatistical modeling of heterogeneous geo-clusters in a copper deposit integrated with multinomial logistic regression: An exercise on resource estimation

Resource estimation is the main and primary step in the development of a mining project. Principally, it is necessary to first identify the geological domains through boreholes, model them at unsampled locations, and then evaluate the grade(s) of interest inside each built domain. The traditional determination of these categorical domains over the sampling points is suboptimal as it considers mostly-one or two variables from core logging. This leads to the neglect of the influence of other significant variables. To circumvent the problem of estimation domain identification, spatially dependent clustering machine learning algorithms can be of great help in detecting such domains. However, one problem that may appear when using these techniques is that the resulting geo-domains (geo-clusters) obtained by the clustering technique might be heterogeneous and show a non-stationary property. The reason is that the aim of these spatially dependent techniques is to produce compact and spatially contiguous clusters, which are well suited to establishing non-stationary geo-domains. This makes the procedure of modelling challenging as it necessitates the use of advanced geostatistical techniques to propagate the heterogeneous geo-clusters at unsampled locations.An algorithm is presented in this study that employs a non-stationary sequential indicator simulation paradigm to model such complex variability of heterogeneous geo-clusters. Since the spatial trends of underlying geo-clusters are required in this simulation method, in this study, we propose the use of multinomial logistic regression to infer these trends. The algorithm was tested using an actual case study from a porphyry copper deposit in Iran, where Cu, Mo, Au, Rock Quality Designation (RQD), mineralization zones, alteration types, and rock types were employed to identify and spatially model the heterogeneous geo-domains in the entire deposit. The results were compared with a conventional sequential indicator simulation where no trend was used. An examination of the resulting maps using several evaluation criteria including visual inspection of the realizations, probability maps, reproduction of proportion of each geo-cluster, connectivity measures, and trend analysis, showed that the findings of the proposed algorithm were superior in modelling heterogeneous geo-domains.

Analysis of the diversity and distributional patterns of coleopteran families on a global scale.

Invertebrates make up the majority of all living species on earth. Nevertheless, our understanding of the global distribution of terrestrial biodiversity and regional patterns therein has thus far been almost entirely based on vertebrate and vascular plant patterns. Here we try to provide some information on the global biogeography of the largest invertebrate order, the beetles (Coleoptera). We compile and analyse a database of beetle distributions, containing presence-absence data for 177 coleopteran families across 827 ecoregions. We map family richness and weighted endemism and find the highest values in the Neotropics and Southeast Asia, but also in the temperate northern hemisphere. Important centres of beetle family endemism include the western and southern parts of North America, Central America, temperate South America, Europe, South, Southeast and East Asia, and eastern Australia. A series of UPGMA cluster analyses were used to produce two global regionalisation maps. As analyses on the complete dataset failed to produce contiguous clusters, two sub-datasets were considered. The first one excluded widespread families and family-poor ecoregions, and the second was restricted to endemic-rich ecoregions. The clusters resulting from the first analysis are partly similar to vertebrate- and plant-based regionalisation schemes, with easily discernible and extensive Holarctic and Holotropical regions, but also include numerous smaller regions, mostly in the temperate parts of the southern Hemisphere (Andean, Patagonian, Matorral, New Guinean, New Caledonian, southern African), with Australia split between a tropical and a temperate/subtropical part. New Zealand is not analysed in this reduced dataset, but clusters with New Caledonia in the endemic-rich analysis, where further interesting subdivisions emerge (mainly in East Asia and western North America). We discuss these patterns in the light of coleopteran dispersal and the habitat requirements predominant across the coleopteran phylogeny, while considering the age of beetle families.

Sustainable Development of Life Service Resources: A New Framework Based on GIScience and Spatial Justice

The sustainable development goals (SDGs) reflect the pursuit of achieving spatial justice. Both SDG 1.4 and SDG 11.1 reflect a concern for urban services. Life service resources, which are the new concept proposed by the Chinese government, also call for sustainable development path. However, few studies have focused on the realization of spatial justice in life service resources. This paper proposes a two-level, four-step analysis framework composed of quantity, structure, pattern, and coupling coordination to perceive the spatial justice of life service resources. Based on remote sensing technology and geographic information science, this paper acquires and analyses multi-source data including population density, building outlines, point of interests, subway lines, etc. Furthermore, the case study in downtown Beijing found the following: (1) The total life service resources are extensive and varying in type; (2) regional differences are evident and low-level equilibrium and high-level priority development coexist; (3) life service resources are concentrated in contiguous and multi-centre clusters with a greater north–south than east–west difference; (4) the overall level of life service resources is low, specifically for “high in the centre and low in the periphery” and “high in the east and low in the west”. Future management should consider narrowing the development gap and formulating industry development plans to improve spatial justice. Finally, the comparison between Beijing and London and more cities in the future needs to consider the urban development stage, population density, and other aspects.

Detecting Square Grid Structure in an Animal Neuronal Network.

An animal neural system ranges from a cluster of a few neurons to a brain of billions. At the lower range, it is possible to test each neuron for its role across a set of environmental conditions. However, the higher range requires another approach. One method is to disentangle the organization of the neuronal network. In the case of the entorhinal cortex in a rodent, a set of neuronal cells involved in spatial location activate in a regular grid-like arrangement. Therefore, it is of interest to develop methods to find these kinds of patterns in a neural network. For this study, a square grid arrangement of neurons is quantified by network metrics and then applied for identification of square grid structure in areas of the fruit fly brain. The results show several regions with contiguous clusters of square grid arrangements in the neural network, supportive of specialization in the information processing of the system.