The Influence of Green Space Patterns on Land Surface Temperature in Different Seasons: A Case Study of Fuzhou City, China

Background: Urban green space (UGS) has been shown to play an important role in mitigating urban heat island (UHI) effects. In the context of accelerating urbanization, a better understanding of the landscape pattern mechanisms affecting the thermal environment is important for the improvement of the urban ecological environment. Methods: In this study, the relationship between land surface temperature (LST) and the spatial patterns of green space was analyzed using a bivariate spatial autocorrelation and spatial autoregression model in three seasons (summer, transition season (spring), and winter) with different grid scales in Fuzhou city. Results: Our results indicated that the LST in Fuzhou City has a significant spatial autocorrelation. The percentage of landscape and patch density area were negatively correlated with surface temperature. The results of our indicators differed according to the season, with population density and distance to the water indicators not being significant in the winter. The coefficient of determination was higher at the 510 m grid scale on this study’s scale. Conclusion: This study extends our understanding on the influence of UHI effects after accounting for different seasonal and spatial scale factors. It also provides a reference for urban planners to mitigate heat islands in the future.

Diverse neighbors and post-conflict recovery at the village level: Evidence from Iraq after ISIL

Why do some towns recover faster than others after intrastate conflict? Many important decisions about post-conflict recovery are made at the substate level, but little empirical work has investigated what causes differences in recovery outcomes within a country. This article suggests that proximity to ethno-religiously diverse neighbors slows a town’s post-conflict recovery. A town has ‘diverse neighbors’ if towns with different plurality ethno-religious groups are nearby. This hypothesis is tested by exploring variation in recovery speed among Iraqi towns after the 2014–17 Islamic State insurgency (ISIL). The article constructs 81-month panels of economic activity for 379 Iraqi settlements occupied by ISIL by using satellite-observed nighttime light emissions as a proxy for economic activity. The panels reveal large variation in post-conflict recovery among towns during the first year of peace. Village-level survey data are then used to construct a measure of neighbor diversity, which is combined with lighting-based recovery scores in spatial autoregression. The results show that greater neighbor diversity is robustly associated with slower settlement recovery. The neighbor diversity penalty cannot be fully explained by cleavages between groups ‘on opposite sides’ of the conflict; proximity to out-group neighbors appears to slow recovery even between wartime allies. Several explanations are considered, and this article suggests that the types of post-liberation controllers that arise in diverse areas – which tend to be substate militias rather than the government – may be one important mechanism.

Identification Of Multifunctional Urban Activity Centers In Tokyo

Identification of urban activity centers is among the most important components of the urban structure study, it is necessary for reasonable planning, regulation of traffic flows and other practical measures. The purpose of this paper is to design a complex method to identify urban activity centers based on different but universal data types. In this study, we used social media data (Twitter) since it guarantees regular updates and does not rely on administrative borders and points of interest database that was considered a 'hard' representation of multifunctional urban activities. A large amount of geotagged tweets was processed by means of statistical modelling (spatial autoregression) and combined with the distribution analysis of points of interest. This allowed to identify the local centers of urban activity within 23 special wards of Tokyo more objectively and precisely than when only based on the social media data. Thereafter, delimitated centers were classified in order to define and describe their main functional and spatial characteristics. As a result of the study, railway transport was identified as the main attraction factor of the urban activity; the modern urban structure of Tokyo was identified and mapped; a new comprehensive method for identification of urban activity centers was developed and five classes of urban activity centers were defined and described.

Developing a Framework for Spatial Effects of Smart Cities Based on Spatial Econometrics

The rapid urbanization in China has already put heavy pressures on imperfect infrastructure, especially for fundamental urban functions such as power and water supply, traffic, education, and healthcare. The emergence of smart cities can help cope with the rapidly expanding demands on urban infrastructure. However, the development of smart cities in China is just in its infancy, and there is still a lack of clear understanding of the development path of smart cities. This article focuses on the development of smart cities in China. It aims to (a) judge whether there is spatial autoregression in the construction of smart cities in 83 Chinese cities and (b) identify key influencing factors in the development of smart cities in China through a spatial econometric model developed by GeoDa software. The results show that there exists spatial autoregression in the development of smart cities in China. Four key influencing factors (governmental support, innovative level, economic development, and human capital) are identified. Based on these findings, suggestions for future promoting development of smart cities in China are put forward. This research can deepen the understanding of the spatial effects of smart cities and provide valuable decision-making references for policy makers.

Travel and us: The impact of mode share on sentiment using geo-social media and GIS

Commute stress is a serious health problem that impacts nearly everyone. Considering that microblogged geo-locational information offers new insight into human attitudes, the present research examined the utility of geo-social media data for understanding how different active and inactive travel modes affect feelings of pleasure, or displeasure, in two major U.S. cities: Chicago, Illinois and Washington D.C. A popular approach was used to derive a sentiment index (pleasure or valence) for each travel Tweet. Methodologically, exploratory spatial data analysis (ESDA) and global and spatial regression models were used to examine the geography of all travel modes and factors affecting their valence. After adjusting for spatial error associated with socioeconomic, environmental, weather, and temporal factors, spatial autoregression models proved superior to the base global model. The results showed that water and pedestrian travel were universally associated with positive valences. Bicycling also favorably influenced valence, albeit only in D.C. A noteworthy finding was the negative influence temperature and humidity had on valence. The outcomes from this research should be considered when additional evidence is needed to elevate commuter sentiment values in practice and policy, especially in regards to active transportation.

Travel and us: The impact of mode share on sentiment using geo-social media and GIS

Commute stress is a serious health problem that impacts nearly everyone. Considering that microblogged geo-locational information offers new insight into human attitudes, the present research examined the utility of geo-social media data for understanding how different active and inactive travel modes affect feelings of pleasure, or displeasure, in two major U.S. cities: Chicago, Illinois and Washington D.C. A popular approach was used to derive a sentiment index (pleasure or valence) for each travel Tweet. Methodologically, exploratory spatial data analysis (ESDA) and global and spatial regression models were used to examine the geography of all travel modes and factors affecting their valence. After adjusting for spatial error associated with socioeconomic, environmental, weather, and temporal factors, spatial autoregression models proved superior to the base global model. The results showed that water and pedestrian travel were universally associated with positive valences. Bicycling also favorably influenced valence, albeit only in D.C. A noteworthy finding was the negative influence temperature and humidity had on valence. The outcomes from this research should be considered when additional evidence is needed to elevate commuter sentiment values in practice and policy, especially in regards to active transportation.

Spatial Analysis of Fiscal Balance Fund on Income Inequality in West Kalimantan

The research aims to analyze the effect of fiscal balance fund on income inequality in West Kalimantan by considering spatial inter-relationships between existing districts/cities. The study showed that the Spatial Durbin Model with fixed effect was empirically suitable. A variant of spatial autoregression model using Gini Ratio during the period of 2010 – 2018 in 14 districts/cities of West Kalimantan. The study concludes that income disparities between districts/cities were low and constant or the income was relatively distributed per capita. Spatial interactions between districts/cities and their neighbors are also relatively low. Spatial aspect, fiscal balance fund and regional minimum wage have a significant negative effect. On the contrary, the industrial workforce, educated workforce and medical personnel do not affect income inequality in West Kalimantan. This study provides academics with the understanding of the importance of spatial dependence in income inequality model because the economic activity is always related to the neighbor.Keywords: fiscal balance fund, income inequality, spatial aspect Analisis Spasial Dana Perimbangan Terhadap Disparitas Pendapatan Kalimantan BaratAbstrak: Penelitian ini bertujuan menganalisis pengaruh dana perimbangan terhadap disparitas pendapatan di Kalimantan Barat dengan mempertimbangkan keterkaitan spasial antar kabupaten/kota yang ada. Studi ini menghasilkan pemilihan model spasial durbin dengan efek tetap secara empiris sudah tepat. Variansi dari model autoregresif spasial menggunakan Indeks Gini kurun waktu 2010–2018 silang tempat dari 14 kabupaten/kota di Kalimantan Barat. Hasil penelitian menyimpulkan disparitas pendapatan antar kabupaten/kota rendah dan konstan atau relatif merata dalam pendapatan per kapita. Interaksi spasial antar kabupaten/kota dengan tetangganya juga relatif rendah. Aspek spasial, dana perimbangan dan UMR secara negatif signifikan mempengaruhi disparitas pendapatan. Sedangkan tenaga kerja industri, tenaga kerja terdidik dan tenaga medis tidak mempengaruhi disparitas pendapatan di Kalimantan Barat. Penelitian ini memberikan wawasan bagi kalangan akademisi tentang pentingnya memasukkan spatial dependence kedalam model ketimpangan pendapatan karena proses kegiatan ekonomi selalu berkaitan dengan wilayah tetangga.Kata kunci: dana perimbangan, disparitas pendapatan, aspek spatial

Partisan Predictors for Collective Bargaining Agreement Items

The United States is rare among nations in its highly decentralized process for negotiating collective bargaining agreements with local teachers’ unions. To determine whether partisanship can predict these highly localized decisions, I construct an original database of Pennsylvania collective bargaining agreements (CBAs) merged with publicly available voter registration records to predict the presence of high-profile contract items. Using spatial autoregression and probit regression, I reveal that the partisanship of a school district is a significant predictor for fewer seniority protections but not for lower salaries. These partisan relationships can guide both district administrators and union leaders in future negotiations.