Weighted Mean Center Research Articles

Arah Ekspansi Minimarket Prima Freshmart di Jabodetabek Cenderung ke Selatan

This study aims to identify the spatial distribution of Prima Freshmart (PFM) minimarkets in Jabodetabek from 2013 to 2023 and the factors driving their dispersion. The methods used include: (1) weighted mean center and spatial standard deviation to determine the center and direction of distribution, and (2) geographically weighted regression (GWR) with minimarket distribution as the dependent variable, and population density, distance to toll roads, and distance to city centers as independent variables. Data were processed using Geographic Information System (GIS). The results show that (1) PFM minimarkets were predominantly distributed southward from 2013 to 2023, and (2) higher local R² values were observed in Bekasi Regency, the borders of Bogor Regency, South Tangerang, and Tangerang Regency, as well as the northern part of Tangerang Regency. The spatial distribution of PFM indicates that store location expansion strategies align with population growth patterns in suburban areas of Jabodetabek.

Analyzing spatiotemporal species spread by three declustering methods utilizing monitoring data based on national programs and citizen science

Systematic and dynamic monitoring of species is often performed at preferential locations. Thus, while models of spatial distributions assume evenly distributed spatial sampling, sampling designs are often clustered, resulting in biased estimates. Therefore, spatial declustering methods should be used to reduce bias, for example when quantifying the spread of invasive species. However, little is known about the relative efficiency of such methods. In this study, the spatiotemporal spread of a species was estimated from synthetic data and from field-collected data, using three alternative declustering methods. We compared two grid-based methods that involve data depletion, and a polygon area weighting method (Multiplicatively Weighted Buffer-Thiessen Polygons; MWBTP), in which constrained weights account for spatial redundancy without any loss of data. We applied the effective range radius method to estimate spread rate from range areas in different time steps. The spread rate was determined as a linear regression slope between the effective range radius (range area/π) and time. Range areas were produced using 95% isopleth of kernel density estimation, with either fixed or adaptive bandwidths. Directional spread was quantified using the displacement of the population's weighted mean centers over time, and the direction of spread was determined using their standard deviational ellipse. We simulated spatially clustered sampling data, and compared the spread rates based on each of the three declustering methods, relative to that measured under an evenly distributed sampling. MWTBP best estimated the spread rate using adaptive-bandwidth KDE (−2.2% relative difference from the even distribution), whereas the clustered distribution produced a −34.4% relative difference. The grid-based methods produced relative differences of −19.6% and −28.5%. Our field data span two years from an Israeli national monitoring program of the invasive red palm weevil, Rhynchophorus ferrugineus, in which weevil traps were distributed in clusters. The grid-based methods showed small relative differences of up to 2% in spread rate, compared to uncorrected estimates, with MWBTP resulting in the greatest difference (−7% relative difference). The spread direction as evaluated by MWBTP deflected 18.3° from that of the uncorrected method and best reflected the actual spread direction. In conclusion, MWBTP appears to best overcome clustering, and yields superior or similar results to those of the other, more commonly-used methods. Databases from field monitoring programs are accumulating at accelerating rates, commonly with spatial clustering. Thus, we suggest further development and application of declustering methods on such spatial data to reduce biases in ecological and agricultural research and practice.

Spatiotemporal Analysis for COVID-19 Delta Variant Using GIS-Based Air Parameter and Spatial Modeling.

The coronavirus disease of 2019 (COVID-19) pandemic is currently a global challenge, with 210 countries, including Indonesia, seeking to minimize its spread. Therefore, this study aims to determine the spatiotemporal spread pattern of this virus in Surabaya using various data on confirmed cases from 28 April to 26 October 2021. It also aims to determine the relationship between pollutant parameters, such as carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), and ozone (O3), as well as the government’s high social restrictions policy in Java-Bali. Several methods, such as the weighted mean center, directional distribution, Getis–Ord Gi*, Moran’s I, and geographically weighted regression, were used to identify the spatial spread pattern of the virus. The weighted mean center indicated that the epicenter location of the outbreak moved randomly. The directional distribution demonstrated a decrease of 21 km2 at the end of the study phase, which proved that its spread has significantly reduced in Surabaya. Meanwhile, the Getis–Ord Gi* results demonstrated that the eastern and southern parts of the study region were highly infected. Moran’s I demonstrate that COVID-19 cases clustered during the spike. The geographically weighted regression model indicated a number of influence zones in the northeast, northwest, and a few in the southwest parts at the peak of R2 0.55. The relationship between COVID-19 cases and air pollution parameters proved that people living at the outbreak’s center have low pollution levels due to lockdown. Furthermore, the lockdown policy reduced CO, NO2, SO2, and O3. In addition, increase in air pollutants; namely, NO2, CO, SO2 and O3, was recorded after 7 weeks of lockdown implementation (started from 18 August).

Spatial Pattern Changing Analysis of Built-up Due to The New Era of Aerotropolis in Kulon Progo, D.I. Yogyakarta

Built-up is artificial land use representing urban-oriented activities according to the sector developed in the aerotropolis, the development concept of city activity centered on the globally networked economy’s airport. The growth of unplanned built-up often caused environmental deterioration. Therefore, the impact of the emerging of a new growth center to the growth characteristic of the built-up requires to be analyzed. This research aimed to examine the growth and spatial distribution characteristics of built-up to mitigate the undesirable impact on the environment in the early phase of aerotropolis in Kulon Progo. The growth was analyzed using the spatial intersection of land use/land cover. The distribution orientation growth was carried out by weighted mean center and spatial standard deviational ellipse. The results demonstrate that annual growth of built-up increased from 55.35 ha per year in 2010-2015 to 69.83 ha per year in 2015-2020. Dryland agriculture was the most land use converted to built-up after aerotropolis era. The direction of built-up growth also shifted from northwest to southwest, and the distance between the airport and the mean center was getting closer. Therefore, land use planning policy need to refer to this pattern changing to preserve environmental sustainability while achieving economic growth.

Spatial Weighted Mean Center for Infection, Recovered and Death Cases of COVID-19 in the Kingdom of Saudi Arabia

هدفت الدراسة إلى تحديد مركز ثقل الإصابة والتعافي والوفاة بفيروس كوفيد-19 على مستوى المملكة، وتتبع تحرك مركز ثقل الإصابة والتعافي والوفاة بفيروس كوفيد-19 على مستوى المملكة ثم الكشف عن العوامل التي قد تؤثر في تحديد ثقل ومسار الوباء خلال فترة الدراسة. تبنت الدراسة المنهج الكمي وذلك من خلال تطبيق أسلوب التحليل المكاني (Spatial Analysis) الذي يتناول الظاهرة المكانية بالدراسة والتحليل للخروج بنتائج تفسر هذه الظاهرة باستخدام المركز المتوسط الموزون الذي يعد أحد أهم أدوات قياس التوزيع المكاني للظواهر الجغرافية، وخرجت الدراسة بعدد من النتائج من أهمها تركز قيم المركز المتوسط المكاني الموزون لحالات الإصابة إلى الشرق من المركز المتوسط لمواقع الإصابات، فيما تركزت قيم المركز المتوسط الموزون لحالات التعافي إلى الشرق من المركز المتوسط لمواقع حالات التعافي من الفيروس، وبشكل مغاير تركزت قيم المركز المتوسط الموزون لحالات الوفاة إلى الغرب من المركز المتوسط لمواقع حالات الوفاة متأثراً بأعداد الوفيات المسجلة بمدينة مكة المكرمة وجدة والطائف بمنطقة مكة المكرمة.

Spatiotemporal Assessment of COVID-19 Spread over Oman Using GIS Techniques

Coronavirus disease (COVID-19), caused by acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a worldwide challenge effecting millions of people in more than 210 countries, including the Sultanate of Oman (Oman). Spatiotemporal analysis was adopted to explore the spatial patterns of the spread of COVID-19 during the period from 29th April to 30th June 2020. Our assessment was made using five geospatial techniques within a Geographical Information System (GIS) context, including a weighted mean centre (WMC), standard deviational ellipses, Moran’s I autocorrelation coefficient, Getis-Ord General-G high/low clustering, and Getis-Ord G_{i}^{*} statistic. The Moran’s I-/G- statistics proved that COVID-19 cases in datasets (numbers of cases) were clustered throughout the study period. The Moran’s I and Z scores were above the 2.25 threshold (a confidence level above 95%), ranging from 2274 cases on 29th April to 40,070 cases on 30th June 2020. The results of G_{i}^{*} showed varying rates of infections, with a large spatial variability between the different wilayats (district). The epidemic situation in some wilayats, such as Mutrah, As-Seeb, and Bowsher in the Muscat Governorate, was more severe, with Z score higher than 5, and the current transmission still presents an increasing trend. This study indicated that the directional pattern of COVID-19 cases has moved from northeast to northwest and southwest, with the total impacted region increasing over time. Also, the results indicate that the rate of COVID-19 infections is higher in the most populated areas. The findings of this paper provide a solid basis for future study by investigating the most resolute hotspots in more detail and may help decision-makers identify targeted zones for alleviation plans.

Variability in the Position of the IBEX Ribbon over Nine Years: More Observational Evidence for a Secondary ENA Source

Abstract The ribbon of enhanced energetic neutral atom flux, discovered by the Interstellar Boundary Explorer (IBEX) in 2009, has redefined our understanding of the heliosphere’s interaction with the local interstellar medium (LISM). Yet, its origin continues to be a topic of scientific debate. The ribbon is circular and traces the region where the putative LISM magnetic field (B LISM) is perpendicular to the radial direction from the Sun. Using nine years of IBEX-Hi observations, we investigate the ribbon circularity and location as functions of time and energy. We provide updated locations of the ribbon center at five energy passbands (centered at 0.7, 1.1, 1.7, 2.7, and 4.3 keV) in ecliptic coordinates [longitude, latitude]: [217.°41 ± 0.°95, 44.°36 ± 0.°93], [219.°72 ± 0.°95, 41.°50 ± 0.°87], [220.°51 ± 1.°19, 39.°96 ± 1.°00], [218.°08 ± 1.°66, 38.°44 ± 1.°24], and [214.°68 ± 1.°48, 34.°13 ± 1.°19] respectively. The weighted mean center location over all energies and all years is [218.°33 ± 0.°68, 40.°38 ± 0.°88] and its radius is 74.°81 ± 0.°65. As viewed by IBEX at 1 au, we find that (1) the ribbon is stable over time, with distinct centers at each energy; (2) ribbon centers exhibit small temporal variations, likely caused by the solar wind (SW) speed and density variations; and (3) ribbon location in the sky appears to be driven by (i) the inherent alignment of the ribbon centers along the plane connecting the presumed B LISM and the heliospheric upwind direction, and (ii) the variable SW structure along the heliographic meridian, further emphasizing that the ribbon source is outside the heliosphere.

Relationship of carbon emissions and economic growth in China's construction industry

Coordinating the dilemma of economic development and reducing carbon emissions is of great significance to reaching China's energy-saving and emission-reduction targets. This paper investigates the decoupling relationship between economic growth and carbon emissions from the construction industry of China's 30 provinces, and uses the standard deviational ellipse method to explore the spatial evolution of carbon emissions and the economy. The results indicate that the economic development levels of most provinces were positively correlated with carbon emissions. The spatial differences in the decoupling state of provincial construction industry are significant, and the decoupling states of the same type exhibited a certain spatial aggregation phenomenon. Furthermore, the spatial distribution of the output value and carbon emissions exhibited a northeast-southwest pattern. The weighted mean centers of both were located in the east and moved towards the northwestern region. These results may provide a basis for assessing regional construction carbon emissions and formulating strategies for the coordinated development of low carbon emissions in the construction industry.

In the Footsteps of Mackintosh and Innis: Tracking Canada’s Economic Centre of Gravity since the Great Depression

In this article, we track the historical movement in Canada’s economic centre of gravity (ECG) since the early 20th century. A simple weighted mean centre approach is applied to a novel data set containing long-run estimates of household income at the provincial level. The analysis reveals that from 1926 to 2013, Canada’s ECG shifted westward by a net distance of 204 kilometres. Concomitant with this broad geographical shift, we identify six distinct historical moments during which major spatial realignments occurred, including a significant west-to-east move during the Great Depression years and two important east-to-west shifts during the energy-driven resource booms of the 1970s and post-2000 period. Time series modeling confirms that the ups and downs of resource commodity prices are indeed strongly associated with historical movements of the ECG in Canada.

Material Flow and Spatial Data Analysis of the Petroleum Use to Carbon Dioxide (CO2) Emissions in Northeast China

SummaryThis study investigated the petroleum consumption and petroleum‐related CO2 emissions (PCOEs) in Northeast China at city level using material flow analysis (MFA) and spatial data analysis (SDA). The petroleum flows for the year 2014 were plotted, and then the spatial patterns, weighted mean centers (WMCs), and spatial autocorrelations of petroleum consumption and PCOEs were calculated, respectively. It was found that Northeast China is a petroleum exploitation‐processing‐export region in China; the total input of petroleum flows comprised two parts—exploitation (about 60%) and import (about 40%). About one third of the total product oil supply flowed into other provinces. In the consumption process, the product oil was dominated by two sectors: the industry sector (45.5%) and the transportation sector (31%). The rate of PCOEs was 36.69 million tonnes in the waste discharge process. Meanwhile, the WMCs of the petroleum consumption and the PCOEs were located in the south of Northeast China. The location of the petroleum pipelines was the factor shown to determine the spatial patterns of petroleum consumption and PCOEs and the hotspots were distributed along the petroleum pipeline, especially in the Circum‐Bohai Sea regions. Economic development in these regions shows a positive dependence on petroleum consumption and generates larger PCOEs. The findings obtained in this study could provide important decision‐making support to low‐carbon development in Northeast China.

Understanding the Dynamics of the Pick-Up and Drop-Off Locations of Taxicabs in the Context of a Subsidy War among E-Hailing Apps

The locations where taxicabs pick up and drop off passengers are crucial to understanding the dynamics of taxi trip demand. Investigating their spatial distribution and derived dynamic features is still a key task in the fields of urban geography and transportation. Such investigations are urgently needed, considering the competition created by new communication technology services, specifically e-hailing apps such as Uber, Didi and Kuaidi. For example, a subsidy war between two e-hailing apps occurred in China in 2014. However, how the pick-up and drop-off locations of taxicabs change during subsidy wars is still an open question. This paper introduces a methodological framework that can be used to derive the pick-up and drop-off dynamics of taxicabs. It also proposes three indexes that can be used to assess the dynamics of the pick-up and drop-off locations at the city and sub-district scales, namely the numbers of daily pick ups and drop offs per taxi, average transfer distance per unit area of weighted mean centers of pick-up and drop-off locations, and degree of dispersion in the spatial distribution of pick-up and drop-off locations. This paper employs data from taxicabs in the city of Shenzhen to uncover the dynamics of their pick-up and drop-off locations. The results show that the methodological framework and the indexes introduced are powerful tools for uncovering the dynamics of the pick-up and drop-off locations of taxicabs in urban environments.

Measuring spatio-temporal dynamics of impervious surface in Guangzhou, China, from 1988 to 2015, using time-series Landsat imagery

This study evaluated the spatio-temporal change characteristics of urban development at different scales with time-series impervious surface fractions. Landsat-5 Thematic Mapper (TM) and Landsat-8 Operational Land Imager (OLI) images were used to extract impervious surface fractions using a modified linear spectral mixture analysis method in Guangzhou from 1988 to 2015. The results indicated that the impervious surface area has substantially increased, from 70.3 km2 in 1988 to 580.5 km2 in 2015. In 2015, the impervious surfaces were distributed almost throughout the whole region of the study area, except in the forest region. Next, impervious surface weighted mean centre (ISWMC) and the standard deviational ellipse (SDE) methods were used to systematically analyse the principle orientation, direction, spatio-temporal expansion trends, and the distribution differences of impervious surfaces at the whole and local region scales from 1988 to 2015. The spatio-temporal dynamics of ISWMC exhibited different expansion directions and intensities of impervious surfaces at the whole and local region scales. On a whole region scale, the principle expansion direction of impervious surfaces was northward. However, the expansion trend of impervious surfaces in the different districts was significantly different from other trends at the local region scale. The parameters of SDE were used to investigate the orientation and the clustering or dispersion degree of impervious surface at different scales. The results from SDE analysis indicated that the impervious surfaces exhibited uncertainty in the expansion direction at the whole region scale; in contrast, they had a distinct preferred orientation and expansion direction at the local region scale. The analysis revealed that urban expansion exhibited different change characteristics in various directions at the local region scale. In summary, the results at the local region scale can better reflect the change trajectory of spatio-temporal dynamics of urban development and its fine spatial structure than at the whole region scale.

Exploratory Spatiotemporal Analysis in Risk Communication during the MERS Outbreak in South Korea

The 2015 Middle East respiratory syndrome (MERS) outbreak in South Korea gave rise to chaos caused by psychological anxiety, and it has been assumed that people shared rumors about hospital lists through social media. Sharing rumors is a common form of public perception and risk communication among individuals during an outbreak. Social media analysis offers an important window into the spatiotemporal patterns of public perception and risk communication about disease outbreaks. Such processes of socially mediated risk communication are a process of meme diffusion. This article aims to investigate the role of social media meme diffusion and its spatiotemporal patterns in public perception and risk communication. To do so, we applied analytical methods including the daily number of tweets for metropolitan cities and geovisualization with the weighted mean centers. The spatiotemporal patterns shown by Twitter users' interests in specific places, triggered by real space events, demonstrate the spatial interactions among places in public perception and risk communication. Public perception and risk communication about places are relevant to both social networks and spatial proximity to where Twitter users live and are interpreted in reference to both Zipf's law and Tobler's law.

Characterizing spatial-temporal changes of heat waves in China using center of gravity analysis

Many regions of the world have experienced dramatic changes in occurrence of heat waves. Investigating changes in their spatial patterns is of great importance for understanding the causes of heat waves and their related processes. This paper, based on observation data collected from 611 meteorological stations, used the weighted mean center tool in ArcGIS to calculate China’s heat wave center of gravity and to trace its migration over the past five decades. The results show an association between its north-south movements and the change of Arctic Oscillation Index, especially for the period after the mid-1980s, during which substantial north-south displacements took place. This study also revealed a close relationship between its west-east movements and the change in annual number of heat wave days before the 1990s, after which it was replaced by a time-dependent relationship that showed increasing heat wave trend only.

Multi-model comparison on the effects of climate change on tree species in the eastern U.S.: results from an enhanced niche model and process-based ecosystem and landscape models

Context. Species distribution models (SDM) establish statistical relationships between the current distribution of species and key attributes whereas process-based models simulate ecosystem and tree species dynamics based on representations of physical and biological processes. TreeAtlas, which uses DISTRIB SDM, and Linkages and LANDIS PRO, process-based ecosystem and landscape models, respectively, were used concurrently on four regional climate change assessments in the eastern United States. Objectives. We compared predictions for 30 species from TreeAtlas, Linkages, and LANDIS PRO, using two climate change scenarios on four regions, to derive a more robust assessment of species change in response to climate change. Methods. We calculated the ratio of future importance or biomass to current for each species, then compared agreement among models by species, region, and climate scenario using change classes, an ordinal agreement score, spearman rank correlations, and model averaged change ratios. Results. Comparisons indicated high agreement for many species, especially northern species modeled to lose habitat. TreeAtlas and Linkages agreed the most but each also agreed with many species outputs from LANDIS PRO, particularly when succession within LANDIS PRO was simulated to 2300. A geographic analysis showed that a simple difference (in latitude degrees) of the weighted mean center of a species distribution versus the geographic center of the region of interest provides an initial estimate for the species’ potential to gain, lose, or remain stable under climate change. Conclusions. This analysis of multiple models provides a useful approach to compare among disparate models and a more consistent interpretation of the future for use in vulnerability assessments and adaptation planning.

Spatial-temporal dynamics of desert vegetation and its responses to climatic variations over the last three decades: a case study of Hexi region in Northwest China

Analysis of spatial-temporal variations of desert vegetation under the background of climate changes can provide references for ecological restoration in arid and semi-arid areas. In this study, we used the Global Inventory Modeling and Mapping Studies (GIMMS) NDVI data from 1982 to 2006 and Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI data from 2000 to 2013 to reveal the dynamics of desert vegetation in Hexi region of Northwest China over the past three decades. We also used the annual temperature and precipitation data acquired from the Chinese meteorological stations to analyze the response of desert vegetation to climatic variations. The average value of NDVImax (the maximum NDVI during the growing season) for desert vegetation in Hexi region increased at the rate of 0.65×10–3/a (P<0.05) from 1982 to 2013, and the significant increases of NDVImax mainly appeared in the typical desert vegetation areas. Vegetation was significantly improved in the lower reaches of Shule and Shiyang river basins, and the weighted mean center of desert vegetation mainly shifted toward the lower reaches of the two basins. Almost 95.32% of the total desert vegetation area showed positive correlation between NDVImax and annual precipitation, indicating that precipitation is the key factor for desert vegetation growth in the entire study area. Moreover, the areas with non-significant positive correlation between NDVImax and annual precipitation mainly located in the lower reaches of Shiyang and Shule river basins, this may be due to human activities. Only 7.64% of the desert vegetation showed significant positive correlation between NDVImax and annual precipitation in the Shule River Basin (an extremely arid area), indicating that precipitation is not the most important factor for vegetation growth in this basin, and further studies are needed to investigate the mechanism for this phenomenon.

Geographical distribution of the association between El Niño South Oscillation and dengue fever in the Americas: a continental analysis using geographical information system-based techniques.

El Niño South Oscillation (ENSO) is one climatic phenomenon related to the inter-annual variability of global meteorological patterns influencing sea surface temperature and rainfall variability. It influences human health indirectly through extreme temperature and moisture conditions that may accelerate the spread of some vector-borne viral diseases, like dengue fever (DF). This work examines the spatial distribution of association between ENSO and DF in the countries of the Americas during 1995-2004, which includes the 1997-1998 El Niño, one of the most important climatic events of 20(th) century. Data regarding the South Oscillation index (SOI), indicating El Niño-La Niña activity, were obtained from Australian Bureau of Meteorology. The annual DF incidence (AIy) by country was computed using Pan-American Health Association data. SOI and AIy values were standardised as deviations from the mean and plotted in bars-line graphics. The regression coefficient values between SOI and AIy (rSOI,AI) were calculated and spatially interpolated by an inverse distance weighted algorithm. The results indicate that among the five years registering high number of cases (1998, 2002, 2001, 2003 and 1997), four had El Niño activity. In the southern hemisphere, the annual spatial weighted mean centre of epidemics moved southward, from 6° 31' S in 1995 to 21° 12' S in 1999 and the rSOI,AI values were negative in Cuba, Belize, Guyana and Costa Rica, indicating a synchrony between higher DF incidence rates and a higher El Niño activity. The rSOI,AI map allows visualisation of a graded surface with higher values of ENSO-DF associations for Mexico, Central America, northern Caribbean islands and the extreme north-northwest of South America.

Spatial distribution and autocorrelation pattern of Shenzhen service industries

©, 2014, Editorial Office of Journal of Shenzhen University. All right reserved. Based on the establishment of Shenzhen service enterprises geographic database, spatial analysis is adopted to develop empirical research on Shenzhen service industry spatial distribution and autocorrelation patterns. Conclusions are drawn as follows: dot density rendering and centrographic statistics estimate the spatial distribution characteristics of geographic factors on the whole. Shenzhen service enterprises distribute with a strong trickle-down trend from the city center to the periphery. The weighted mean centers of Shenzhen service industries display an obviously western offset from the geometry center. Indicated by Moran's I verifications, Shenzhen service industries have different global spatial autocorrelations. Also Shenzhen services with high proportion of added values (≥3%) show diversified local spatial autocorrelations on agglomerative subdistricts. Regional industrial division has been formed, which is the result of cooperative effect of location condition, economic ties, and government planning. Furthermore, it would be necessary to keep customized spatial development policies for the specific service industry, because of the existence of urban regional differences and industrial heterogeneity.

공간분석 기법을 이용한 만리포 유분의 시·공간 변동 패턴 분석

Mallipo is a representative beach contaminated by Hebei Spirit oil spill accident in December 2007. This study aims to compare the differences of two seasons (winter and summer) for the spatio-temporal variation patterns of oiling status in the whole area and divided five regions of Mallipo beach. In the whole area, the decreasing rate of average TPH (total petroleum hydrocarbon) in winter was twice greater than summer during four years. According to the spatial variation pattern analysis of oiling status using weighted mean center and weighted standard distance, the oil concentration was clustered on southwestern region in winter, however, the TPH was dispersed in the whole area in summer. Temporal variation pattern of TPH in each of Mallipo`s five regions showed that TPH had been consistently decreased in winter, but oil concentration had not been changed in summer since 2009 except the southwestern region. Therefore, in order to evaluate and predict the progress of oiling status, it is needed to analyze the spatio-temporal variation pattern of TPH using spatial analysis after separating data into seasons (e.g., winter and summer). In addition, time series analysis is useful in the regional scales through spatial partitioning rather than the whole beach area for the understanding of temporal variation pattern.

공간통계를 이용한 공주시의 시공간적 지가변화패턴 분석

본 연구는 행정중심복합도시 건설에 따라 이에 편입되는 지역과 그 주변지역을 포함한 공주시를 대상으로 지가의 시공간적 변화패턴을 파악하는데 목적이 있다. 이를 위해 2000년, 2005년, 2010년을 기준시점으로 209개의 동/리별 평균지가를 산출하여 GIS데이터를 구축하였다. 분석방법으로는 크게 3가지 측면으로 구분할 수 있다. 먼저 공간통계기법의 일종인 크리깅 보간에 의한 5년 단위의 지가변화 추이를 파악하였다. 둘째, 동서축과 남북축의 방향별 변화패턴을 알아보기 위해 경향분석(trend analysis)을 실시하였다. 셋째, 시점별 지가중심점의 이동 방향을 살펴보기 위해 지가를 가중치로 하여 가중평균중심점(weighted mean center)을 산출하였다. 그 결과 지목(Land Category)별 지가변화추세는 행정중심복합도시에 편입되는 동부지역에서 높은 성장세를 보이는 것으로 나타났고, 중심점의 이동방향 역시 동북부지역으로 편중되는 현상을 보였다. This study aims to identify spatio-temporal land price change pattern in Gongju city including the area incorporated and surrounding area depending on the Multifunctional Administrative City Construction. For this, GIS data was built by calculating the average land price each 209 Dong and Ri by the time of the year 2000, 2005 and 2010 based on. The first, the change in the land price was to identify in the 5-year intervals through a kriging interpolation as a kind of geostatistical techniques. The second, a trend analysis was conducted to know directional change pattern of the east-west axis and the north-south axis. Finally, the weighted mean center was calculated by the land price at a weight to examine moving direction on the center point of land price, point of view. The result is that the land price change pattern appeared visible higher growth on the eastern built in the Multifunctional Administrative City, moving direction on the center point of the land price appeared that the phenomenon was concentrated in the northeastern area.