Factors influencing spatial heterogeneity of female white-tailed deer harvest dynamics

2016 ◽  
Vol 40 (4) ◽  
pp. 758-763 ◽  
Author(s):  
Gabriel R. Karns ◽  
Robert J. Gates ◽  
Stephen N. Matthews ◽  
Jeremy T. Bruskotter ◽  
J. Clint McCoy ◽  
...  
Keyword(s):  
Spatial Heterogeneity ◽  
Factors Influencing ◽  
Deer Harvest

scholarly journals Analysis of Spatial Heterogeneity and the Scale of the Impact of Changes in PM2.5 Concentrations in Major Chinese Cities between 2005 and 2015

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3232
Author(s):  
Feili Wei ◽  
Shuang Li ◽  
Ze Liang ◽  
Aiqiong Huang ◽  
Zheng Wang ◽  
...  
Keyword(s):  
Wind Speed ◽  
Spatial Heterogeneity ◽  
Foreign Investment ◽  
Geographically Weighted Regression ◽  
Weighted Regression ◽  
Concentration Change ◽  
Regional Effects ◽  
Factors Influencing ◽  
The Sichuan Basin ◽  
Pm2.5 Concentration

Deteriorating air quality is one of the most important environmental factors posing significant health risks to urban dwellers. Therefore, an exploration of the factors influencing air pollution and the formulation of targeted policies to address this issue are critically needed. Although many studies have used semi-parametric geographically weighted regression and geographically weighted regression to study the spatial heterogeneity characteristics of influencing factors of PM2.5 concentration change, due to the fixed bandwidth of these methods and other reasons, those studies still lack the ability to describe and explain cross-scale dynamics. The multi-scale geographically weighted regression (MGWR) method allows different variables to have different bandwidths, which can produce more realistic and useful spatial process models. By applying the MGWR method, this study investigated the spatial heterogeneity and spatial scales of impact of factors influencing PM2.5 concentrations in major Chinese cities during the period 2005–2015. This study showed the following: (1) Factors influencing changes in PM2.5 concentrations, such as technology, foreign investment levels, wind speed, precipitation, and Normalized Difference Vegetation Index (NDVI), evidenced significant spatial heterogeneity. Of these factors, precipitation, NDVI, and wind speed had small-scale regional effects, whose bandwidth ratios are all less than 20%, while foreign investment levels and technologies had medium-scale regional effects, whose bandwidth levels are 23% and 32%, respectively. Population, urbanization rates, and industrial structure demonstrated weak spatial heterogeneity, and the scale of their influence was predominantly global. (2) Overall, the change of NDVI was the most influential factor, which can explain 15.3% of the PM2.5 concentration change. Therefore, an enhanced protection of urban surface vegetation would be of universal significance. In some typical areas, dominant factors influencing pollution were evidently heterogeneous. Change in wind speed is a major factor that can explain 51.6% of the change in PM2.5 concentration in cities in the Central Plains, and change in foreign investment levels is the dominant influencing factor in cities in the Yunnan-Guizhou Plateau and the Sichuan Basin, explaining 30.6% and 44.2% of the PM2.5 concentration change, respectively. In cities located within the lower reaches of the Yangtze River, NDVI is a key factor, reducing PM2.5 concentrations by 9.7%. Those results can facilitate the development of region-specific measures and tailored urban policies to reduce PM2.5 pollution levels in different regions such as Northeast China and the Sichuan Basin.

Factors Influencing Willingness to Pay for Deer Hunting in the Alabama Piedmont

1992 ◽  
Vol 16 (3) ◽  
pp. 125-129 ◽  
Author(s):  
H. L. Stribling ◽  
J. P. Caulfield ◽  
B. G. Lockaby ◽  
D. P. Thompson ◽  
H. E. Quicke ◽  
...  
Keyword(s):  
Forest Management ◽  
Willingness To Pay ◽  
Deer Hunting ◽  
Factors Influencing ◽  
Deer Harvest ◽  
Pine Stands

Abstract Fifty-three individuals who hunted deer in the Alabama Piedmont during the 1988-1989 season were interviewed by telephone to determine their willingness to pay for the same hunting land under different hypothetical forest management and deer harvest situations. Willingness to pay significantlydecreased when the percentage of land in recent clearcut or in young pine stands increased beyond 50% of the area. These hunters indicated they would pay the same as they currently do or more for tracts composed of up to 25% young pine or an all-aged mix of pine-hardwood. Individuals not currentlyleasing hunting land were willing to pay more for the possibility of harvesting additional deer. Individuals currently leasing hunting land were not willing to pay a higher price to harvest more deer. South. J. Appl. For. 16(3):125-129.

Factors Influencing Efficiency of White-Tailed Deer Harvest in Illinois

1997 ◽  
Vol 61 (4) ◽  
pp. 1091 ◽  
Author(s):  
John R. Foster ◽  
John L. Roseberry ◽  
Alan Woolf
Keyword(s):  
Factors Influencing ◽  
Deer Harvest

Factors influencing spatial heterogeneity of soil moisture content in a small catchment of Mount Taiyue, Shanxi Province

2017 ◽  
Vol 37 (11) ◽  
Author(s):  
王甜 WANG Tian ◽  
康峰峰 KANG Fengfeng ◽  
韩海荣 HAN Hairong ◽  
程小琴 CHENG Xiaoqin ◽  
白英辰 BAI Yingchen ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Spatial Heterogeneity ◽  
Soil Moisture Content ◽  
Shanxi Province ◽  
Small Catchment ◽  
Factors Influencing

scholarly journals Exploring Spatial Heterogeneity and Factors Influencing Construction and Demolition Waste in China

2021 ◽  
Author(s):  
Zhenshuang Wang ◽  
Zhongsheng Zhang ◽  
Jiangkuang Liu
Keyword(s):  
Spatial Heterogeneity ◽  
Spatial Autocorrelation ◽  
Treatment Strategies ◽  
Construction And Demolition Waste ◽  
Growth Trend ◽  
Demolition Waste ◽  
Positive Role ◽  
Factors Influencing ◽  
Per Capita ◽  
Temporal And Spatial

Abstract Natural disasters, new urbanization and urban renewal activities generated a large amount of construction and demolition waste (C&DW), and managing C&DW has become an urgent problem to be solved in the construction of “Zero-waste cities”. Based on the calculation of C&DW generation in China from 2005 to 2019, this study analyzed the temporal and spatial characteristics of C&DW generation in China, and empirically explored the factors influencing factors C&DW of China using spatial autocorrelation and geographically weighted regression. The results showed that: (1) C&DW generation in China increased every year, and the overall distribution was characterized as “high in the east and low in the west”, with distinct regional differences. The provinces with the highest per capita C&DW generation were Zhejiang, Jiangsu, Beijing, Shanghai, and Fujian. The generation intensity of C&DW in China and all its provinces showed a decreasing trend every year. The regions with rapid growth of C&DW generation in China were concentrated in the eastern coastal areas, with distinct differences between the east and west, and there was significant spatial heterogeneity in the growth trend. (2) There is a significant spatial autocorrelation in C&DW generation in China. Overall, the hot spots for C&DW generation were distributed in Jiangsu, Zhejiang, and Shandong provinces, and the spatial agglomeration effect of C&DW generation in provinces was evident. (3) Factors such as population size, per capita Gross Domestic Product and the scale of the construction industry played a positive role in promoting C&DW generation in each province, whereas labor efficiency played a negative role inhibiting C&DW generation; there was significant temporal and spatial heterogeneity. Finally, differentiation and cross-regional joint treatment strategies according to regional conditions were proposed to achieve precise measures of C&DW reduction management.

scholarly journals Sustainability evaluation and spatial heterogeneity of urban agglomerations: a China case study

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Ruomeng Zhou ◽  
Gang Liu ◽  
Yunsheng Zhang
Keyword(s):  
Sustainable Development ◽  
Spatial Heterogeneity ◽  
Indicator System ◽  
Urban Agglomeration ◽  
Western China ◽  
Western Region ◽  
The Sustainable Development ◽  
Urban Agglomerations ◽  
Factors Influencing ◽  
Development Level

AbstractTo promote the development of the western region in China, it is necessary to build an indicator system to scientifically measure the level of sustainable development in Western China. Based on the construction of a sustainable development level evaluation indicator system, this study employs the panel data entropy model to evaluate the sustainable development level of four state-level urban agglomerations in Western China from 2009 to 2018. Then, the geographical detector model is used to measure the spatial heterogeneity degree of the sustainability index and detect the factors influencing the spatial heterogeneity. The results show that (1) the innovation environment and economic growth are the core factors influencing the sustainable development level. (2) The sustainable development level of the cities within the urban agglomerations varies considerably. The sustainability level of central cities and provincial capital cities is significantly higher than that of outlying cities. (3) From the perspective of time, the sustainable development level of the Chengdu–Chongqing urban agglomeration and Guanzhong Plain urban agglomeration shows a rising trend; the Lanzhou–Xining urban agglomeration fluctuates greatly; and the Hohhot–Baotou–Ordos–Yulin urban agglomeration is decreasing. (4) The spatial heterogeneity level of sustainable development among cities in the western urban agglomerations is high, economic factors play a leading role in the spatial heterogeneity of sustainable development, and the western region needs to emphasize regional coordinated development.

Spatial heterogeneity of factors influencing forest fires size in northern Mexico

2014 ◽  
Vol 25 (2) ◽  
pp. 291-300 ◽  
Author(s):  
Gustavo Perez-Verdin ◽  
Marco Antonio Marquez-Linares ◽  
Maricela Salmeron-Macias
Keyword(s):  
Spatial Heterogeneity ◽  
Forest Fires ◽  
Northern Mexico ◽  
Factors Influencing

scholarly journals Spatial Heterogeneity of the Impact Factors on Gray Water Footprint Intensity in China

2020 ◽  
Vol 12 (3) ◽  
pp. 865
Author(s):  
Lingling Zhang ◽  
Rui Zhang ◽  
Zongzhi Wang ◽  
Fan Yang
Keyword(s):  
Spatial Heterogeneity ◽  
Pollution Control ◽  
Total Population ◽  
Water Footprint ◽  
Factors Influencing ◽  
Gray Water ◽  
Per Capita ◽  
Urbanization Rate ◽  
The Impact ◽  
Capita Output

The gray water footprint intensity represents the amount of freshwater resources that need dilution of pollutants per unit of economic output, which indicates the relationship among water pollution, water resources and economy. In this paper, the gray water footprint of 31 provinces (autonomous regions) in China was estimated based on different water bodies. The spatial pattern and spatial agglomeration characteristics of gray water footprint in China from 2000 to 2014 were explored from the perspective of spatial autocorrelation. By extending the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model, the impact of the total population, urbanization rate, per capita output value, the proportion of the tertiary industry, environmental pollution control intensity and R&D investment intensity on the degree of gray water footprint intensity are explored, with ridge regression analysis to solve the problem of collinearity affecting factors. Meanwhile, the Geographically Weighted Regression (GWR) model is used to detect the spatial heterogeneity and spatio-temporal variation characteristics of the factors influencing gray water footprint intensity among regions. The study found that from 2000 to 2014, the gray water footprint of 31 provinces and cities in China was unstable; the domestic gray water footprint accounted for the largest proportion; the agricultural gray water footprint was mainly derived from nitrogen fertilizer, and the industrial and domestic gray water footprint was mainly derived from ammonia nitrogen. Water pollution varies from east to west. The total intensity of gray water footprint shows a downward trend, which is related to economic development and improvement of technological level. There is a positive correlation between the urbanization rate and the intensity of the gray water footprint. The total population, the per capita output value, the proportion of the tertiary industry, the intensity of environmental pollution control, the intensity of R&D input and the intensity of the gray water footprint are negatively correlated, and the influencing factors boast obvious spatial heterogeneity. The purpose is to reveal the key factors influencing gray water footprint intensity to ensure the sustainable development of economy, resources and environment through the formulation of regional differences in regulation and control policies.

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