Pattern of Distribution of Spatial Phenomena to Communities Prevailing in Mount Gara Using Function L(r)

Abstract The spatial pattern of species is an important feature to understand why these species coexist and remain in position or not, and using the single Ripley function and the L(r) function, we analyzed the spatial pattern of types of broad-leaf tree and tree covers and the needles for mixed brawls in the forests of Mount Gara, using PASSAGE V.2, L(r) analysis of the species under study showed a variation in the pattern distribution of trees and gave the highest percentage of random form distribution pattern with a cluster pattern of 11.25%, Through the ratios and forms of distribution of the L(r) function of the various samples of the study, we find that these stands generally tend to be regular, indicating that these species remain at the end of the life cycle in the structure of a more stable stand.

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Spatial Patterns of Urban Innovation and Their Evolution from Perspectives of Capacity and Structure: Taking Shenzhen as an Example

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi11010007 ◽

2021 ◽

Vol 11 (1) ◽

pp. 7

Author(s):

Erjie Hu ◽

Di Hu ◽

Handong He

Keyword(s):

Spatial Distribution ◽

Spatial Pattern ◽

Distribution Pattern ◽

Spatial Patterns ◽

Spillover Effect ◽

Tertiary Industry ◽

Innovation Capacity ◽

Structure Index ◽

Secondary Industry ◽

Spatial Spillover Effect

Innovation is a key factor for a country’s overall national strength and core competitiveness. The spatial pattern of innovation reflects the regional differences of innovation development, which can provide guidance for the regional allocation of innovation resources. Most studies on the spatial pattern of innovation are at urban and above spatial scale, but studies at urban internal scale are insufficient. The precision and index of the spatial pattern of innovation in the city needs to be improved. This study proposes to divide spatial units based on geographic coordinates of patents, designs the innovation capability and innovation structure index of a spatial unit and their calculation methods, and then reveals the spatial patterns of innovation and their evolutionary characteristics in Shenzhen during 2000–2018. The results show that: (1) The pattern of innovation capacity of secondary industry exhibited a pronounced spatial spillover effect with a positive spatial correlation. The innovation capacity and innovation structure index of the secondary industry evolved in a similar manner; i.e., they gradually extended from the southwest area to the north over time, forming a tree-like distribution pattern with the central part of the southwest area as the “root” and the northwest and northeast areas as the “canopy”. (2) The pattern of innovation capacity of tertiary industry also had a significant spatial spillover effect with a positive spatial correlation. There were differences between the evolutions of innovation capacity and innovation structure index of tertiary industry. Specifically, its innovation capacity presented a triangular spatial distribution pattern with three groups in the central and eastern parts of the southwest area and the south-eastern part of the northwest area as the vertices, while its innovative structure showed a radial spatial distribution pattern with the southwestern part of the southwest area as the source and a gradually sparse distribution toward the northeast. (3) There were differences between the evolution modes of secondary and tertiary industries. Areas with high innovation capacity in the secondary industry tended to be more balanced, while areas with high innovation capacity in the tertiary industry did not necessarily have a balanced innovation structure. Through the method designed in this paper, the spatial pattern of urban innovation can be more precise and comprehensive revealed, and provide useful references for the development of urban innovation.

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Spatial Patterns of Carbon Monoxide Distribution to Traffic Jam in East Jakarta

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/940/1/012010 ◽

2021 ◽

Vol 940 (1) ◽

pp. 012010

Author(s):

A W Ramadhan ◽

A Wibowo ◽

R Saraswati

Keyword(s):

Spatial Pattern ◽

Distribution Pattern ◽

The Other ◽

Emission Model ◽

Analysis Method ◽

Traffic Jam ◽

High Concentrations ◽

Mobile Emission ◽

Spatial Analysis Method ◽

The Mathematical Model

Abstract The rapid growth of cities will certainly also increase traffic jams and emissions in the air. This study aims to analyze the increase in car volume and the CO distribution pattern in East Jakarta. Data for traffic jam patterns were recorded based on Google Maps on weekdays in the morning and evening. The spatial analysis method used to find the CO distribution pattern is the IDW interpolation, and the mathematical model calculates the moving emission based on the distance travelled (VKT). The spatial pattern of CO distribution in 2020 was scattered with high concentrations in Pasar Rebo, Ciracas, Cipayung, Kramat Jati, and Makasar Districts, with CO levels above 4,500 ppm. The spatial pattern of CO distribution from the mobile emission model differs from the air station IDW interpolation. The CO distribution pattern from the mobile emission model is very concentrated in Makassar, and Kramat Jati District was 6,740.91 tons/year. The result concluded that the increase in vehicle volume is not related to the distribution of the CO model from air station IDW interpolation, and the other hand, the congestion pattern was related to the distribution pattern of the CO model from vehicles from the level of congestion.

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Spatial Pattern Analysis on Dengue Cases in Bangi district, Selangor, Malaysia

10.21203/rs.3.rs-32929/v1 ◽

2020 ◽

Author(s):

Nuriah Abd Majid ◽

Ruslan Rainis ◽

Mazrura Sahani ◽

Ahmad Fariz Mohamed ◽

Sarah Aziz Abdul Ghani Aziz ◽

...

Keyword(s):

Spatial Pattern ◽

Distribution Pattern ◽

Pattern Analysis ◽

Nearest Neighbor ◽

Statistical Analyses ◽

Spatial Pattern Analysis ◽

Study Region ◽

Directional Distribution ◽

Standard Distance ◽

Positive Factor

Abstract Background: Dengue outbreak has proliferated around the developing countries, including Malaysia, in recent decades. Thus, understanding the distribution pattern is essential for urbanization livelihood. Method: The objective of this study is to determine the trend of dengue cases reported from year 2014 to 2018 and the spatial pattern for dengue spread with reference to weather elements in Bangi town. Results: Spatial statistical analyses conducted found that the distribution pattern and spatial mean center for dengue cases was clustered at the east of Bangi region. Directional distribution observed that the elongated polygon of dengue cluster stretched from the northeast to the southwest of Bangi district. Standard distance for dengue cases was the smallest for the year 2014 (0.017 m), and the largest was in the year 2016 (0.019 m), whereas dengue cases in year 2015, 2017, and 2018 were measured at 0.018 m. The average nearest neighbor analysis also observed clustered patterns for dengue cases in Bangi district. Pearson’s correlation analysis found that temperature (r = -0.269) was negatively correlated with dengue cases for year 2014 and 2018; however, rainfall amount (r = 0.286) and rain days (r = 0.250) were positively correlated with dengue cases in year 2018.Conclusions: The three spatial statistical analyses (spatial mean center, standard distance, and directional distribution) findings illustrated that the dengue cases from the year 2014 to 2018 are clustered on the northeast to the southwest of the study region. The rainfall element is found to be a significant positive factor correlated for most study years compared to temperature element.

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Life Cycle and Distribution Pattern of Lipoptena cervi (L.) (Dipt., Hippobosc.) on Danish Deer

Oikos ◽

10.2307/3564750 ◽

1964 ◽

Vol 15 (1) ◽

pp. 93 ◽

Cited By ~ 47

Author(s):

Niels Haarløv ◽

Niels Haarlov

Keyword(s):

Life Cycle ◽

Distribution Pattern

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Spatial pattern of dengue cases: An analysis in Bangi District, Selangor, Malaysia

Geospatial health ◽

10.4081/gh.2021.915 ◽

2021 ◽

Vol 16 (1) ◽

Author(s):

Nuriah Abd Majid ◽

Ruslan Rainis ◽

Mazrura Sahani ◽

Ahmad Fariz Mohamed ◽

Sarah Aziz Abdul Ghani Aziz ◽

...

Keyword(s):

Developing Countries ◽

Spatial Pattern ◽

Distribution Pattern ◽

Statistical Analyses ◽

Nearest Neighbour ◽

Study Region ◽

Directional Distribution ◽

Standard Distance ◽

Dengue Outbreaks ◽

Neighbour Analysis

In recent decades, dengue outbreaks have become increasingly common around the developing countries, including Malaysia. Thus, it is essential for rural as well as urbanised livelihood to understand the distribution pattern of this infection. The objective of this study is to determine the trend of dengue cases reported from the year 2014 to 2018 and the spatial pattern for this spread. Spatial statistical analyses conducted found that the distribution pattern and spatial mean centre for dengue cases were clustered in the eastern part of the Bangi region. Directional distribution observed that the elongated polygon of dengue cluster stretched from the Northeast to the Southwest of Bangi District. The standard distance observed for dengue cases was smallest in the year 2014 (0.017 m), and largest in 2016 (0.019 m), whereas in the year 2015, 2017 and 2018, it measured 0.018 m. The average nearest neighbour analysis also displayed clustered patterns for dengue cases in the Bangi District. The three spatial statistical analyses (spatial mean centre, standard distance and directional distribution) findings illustrate that the dengue cases from the year 2014 to 2018 are clustered in the Northeast to the Southwest of the study region.

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Changes in Spatial Pattern of an Intertidal Population of the Nudibranch Mollusc Onchidoris muricata in Relation to Life-Cycle, Mortality and Environmental Heterogeneity

Journal of Animal Ecology ◽

10.2307/3931 ◽

1978 ◽

Vol 47 (1) ◽

pp. 189 ◽

Cited By ~ 11

Author(s):

Christopher D. Todd

Keyword(s):

Life Cycle ◽

Spatial Pattern ◽

Environmental Heterogeneity ◽

Intertidal Population

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Population structure, distribution pattern and microhabitat characteristics of Aglaonema simplex in Pasatan Protected Forest, Jembrana, Bali, Indonesia

Biodiversitas Journal of Biological Diversity ◽

10.13057/biodiv/d180446 ◽

2017 ◽

Vol 18 (4) ◽

pp. 1663-1668

Author(s):

DEWI LESTARI ◽

NI PUTU SRI ASIH

Keyword(s):

Population Structure ◽

Distribution Pattern ◽

Conservation Policy ◽

Air Humidity ◽

Low Light ◽

Soil Humidity ◽

Population Pattern ◽

Pattern Distribution ◽

Over Time ◽

Morisita's Index

Lestari D, Asih NPS. 2017. Population structure, distribution pattern and microhabitat characteristics of Aglaonema simplex in Pasatan Protected Forest, Jembrana, Bali, Indonesia. Biodiversitas 18: 1663-1668. Aglaonema simplex Blume is one species of Aglaonema that is distributed in Bali, Indonesia. This species can be found in the forests of Jembrana, Karangasem, Tabanan, Buleleng and Bangli. There is no recent data for A. simplexâ€™s population and microhabitat in Bali, while this data is needed to develop future conservation policy. By tracking populations over time, ecologists can see how these populations have changed and may be able to predict how they are likely to change in the future. Monitoring the size and structure of populations can also help ecologists to manage populations. This study was aimed to determine the current condition of A. simplex in the Pasatan forest and to find out the population structure, distribution, and its microhabitat characteristics. The study was conducted along two tracks at Pasatan Forest, Bali on June 9- 11, 2015. Data were collected using the purposive quadrant plot method and analyzed descriptively, tabulated in tables and graphs. The population pattern distribution was as defined by standardized Morisita's index and microhabitat differences in both tracks were determined by the Mann-Whitney test in SPSS 16. The total number of A. simplex was 114 individuals. Thirty-seven percent of plants were juveniles, while 36% were mature plants without fruit and 27% were mature plants with fruit. The population structure along the first track was dominated by a mature population of plants without fruit, while fruiting mature plants dominated the structure of the population along the second track. The distribution of the population along the first track was clustered, while distribution along the second track was uniform. A. simplex was found at an altitude of 367- 448 m asl., oil pH of 6.7-6.8, soil humidity of 73%-84%, air temperature 27Â°-28Â° C, air humidity at 80%-86%, with low light intensity of 170-225 lux.

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Exploration on the Spatial Assembling Pattern of Contemporary Rural Settlements Based on GIS --- Taking Diqing Tibetan Autonomous Prefecture as an Example

MATEC Web of Conferences ◽

10.1051/matecconf/201822805001 ◽

2018 ◽

Vol 228 ◽

pp. 05001

Author(s):

Qi Liu ◽

Yiwei Zhang

Keyword(s):

Spatial Distribution ◽

Spatial Pattern ◽

Distribution Pattern ◽

Optimization Design ◽

Rural Settlement ◽

Spatial Distribution Pattern ◽

Practical Significance ◽

Rural Settlements ◽

Gis Technology ◽

Main Method

Rural settlements of China are in the era of rapid information development, experiencing revolutionary changes and cultural breakthroughs. This article takes the main rural settlements in Diqing as examples and uses GIS technology as the main method, analyses spatial distribution and assembling characteristics of rural settlements. Based on this, the article extracts the spatial assembling pattern of Diqing rural settlements. Take the topography, rivers, roads and other factors, this article analyzes the causes of the spatial distribution pattern of contemporary rural settlements. The article argues that it has a great theoretical and practical significance to study the spatial pattern of rural settlements, and points out the necessity of using modern GIS technology in the rural settlement research. This method cannot be only maximum the precise analytical ability of contemporary traditional rural settlement space, but also better serve the adjustment, control and optimization design of contemporary settlements.

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