Thursday, February 29, 2024

Article Review: Cultural Landscape of Ulanbataar, Mongolia

Introduction

            Mongolia is a country with such a diverse history that it is difficult to find a national identity in modern times.  Many people of different cultures from past and present have occupied its territory, including nomadic pastoralists, Mongols, Buddhists, and Soviets.  Regime changes in the country have overturned previous ideologies with such high frequency that a future socio-political trajectory is nebulous (Diener and Hagen 2013, 623).  Most recently, the transition from a Soviet-influenced, communist elite in the 1990s has created a new path for Mongolia in the contemporary global system that integrates various power structures from its past (ibid. 624).  The researchers of this article used a cultural landscape analysis to piece together the cultural friction as it relates to urban design and modern development in Mongolia’s capital, Ulaanbaatar.

 

Literature Review

            Street names, monuments, landmarks, statues, and architecture can all experience alterations in name or form as a new regime replaces an old one (ibid. 625).  Since capital cities are rarely razed after a political or ideological shift, it stands to reason that their landscape becomes a hybrid of the old regime and the new (ibid. 625).  This can ripple across centuries, as new regimes layer themselves on top of older ones, creating hybrids that are multiplied.  Ulaanbaatar is a relatively young city, but it has experienced so many regime changes that the layers have combined into a national identity that doesn’t claim a single cultural form from its history.

 

Background and Geographical Context

Ulaanbaatar is the capital of Mongolia.  It has changed names many times, most recently in 1924 when the Mongolian republic was established (Britannica 2024).  Located between two perennial world powers (China and Russia), it has experienced much social and cultural change over the centuries.  Because of its landlocked location, the country acts as a buffer between its more powerful neighbors, absorbing cultural influences from the areas surrounding it.

 

Methods Used

 The researchers of the article adopted a cultural landscape method of presenting their data.  According to Gomez and Jones (2010, 225), this involves searching through historical archives to document “when and where the landscape was created, by whom, why, and how [it has] been altered…”.  A cultural landscape continuum consists of historical and contemporary snapshots of accumulated historical occupance (ibid. 227), normally at an urban location.  Methods for retrieving this information may involve investigating historical documents, public records, or for contemporary snapshots, fieldwork, surveying, and interviewing (ibid. 227). 

The authors in this study mostly used written historical documents to illustrate the cultural landscape of Ulaanbaatar.  Historical documents come in two types: primary or secondary.   Primary documents are first-hand accounts of events, while secondary documents are second-hand perspectives, often written by a contemporary historian or journalist who doesn’t directly experience the events.

The documents used in this study were primarily secondary.  They describe how Ulaanbaatar first appeared as a monastery for Buddhists in 1639 (ibid. 626).  Trade helped establish permanent settlements in the region, which had been suited to nomadic peoples due to its extensive grasslands.  As trade increased and elites gained wealth, sedentarism became more common (ibid. 626), creating the first seed of a national identity based on Buddhist culture.  Chinese merchant settlement increased during the Qing dynasty, from 1691 to 1911, resulting in structures that hybridized Mongolian, Tibetan, and Chinese styles (ibid. 627-628).  Tsarist Russia began influencing the region in 1863, starting a trend toward international geopolitical development (ibid. 627).  Soviet socialism ignited a big change on the city in the 1920s, transforming many buildings that were formerly religious into social services, like hospitals and schools (ibid. 628).  Street names were also changed to reflect the new leadership.  Since the collapse of the Soviet Union, the cultural landscape has morphed into a more integrated one that reunites the various ruling elements of the country’s past.  This came about because globalization championed a more democratic, Western ideology that valued freedom, expanding business and tourism in the city (ibid. 638).

 

Analysis and Discussion

            As with most historical or cultural landscape analyses, the study had less emphasis on quantitative data and more on qualitative explanations.  The idea was to paint a narrative of the built environment of Ulaanbaatar from an historical perspective as it changed leadership between eras.  It was thoroughly demonstrated how the Mongolian national image has evolved as each new power occupied its territory.  Photographs were used to strengthen the analysis by giving the visual landscape of Ulaanbaatar a higher understanding.

            One challenge I have for the method is that the iconography of a city is generally constructed by those in power and not those who have been marginalized.  As the victors of history generally write it, the conquerors of a territory transform its cultural landscape.  We see this in the way the Mongolian image largely borrows elements from its ruling powers and not the indigenous nomads who lived there for centuries.  The Mongols, widely regarded as founders of the nation, were the first in a series of ruling classes that shaped its landscape, and they’d conquered it long before 1639- the earliest date that is referenced in this article.  Yet little is known about the nomadic tribes who preceded them, for the consolidation of their tribes is what became the Mongols as we know them today (Bawden 2024).

 

Conclusion

            The researchers of this article did a fantastic job in providing a cultural background of the landscape of Ulaanbaatar.  Despite the dominating iconography of the ruling class, some believe that modernization in Mongolia “calls for the marginalization of nomadic values in the new national ideology” (ibid. 643).  How much this translates to street names, buildings, and statues named in honor of nomads remains to be seen.  Globalization remains a powerful force on the cultural landscape of Ulaanbaatar, one in which traditional nomads will once again struggle to reconcile. 


Bibliography

 

Bawden, C. R.. "Genghis Khan." Encyclopedia Britannica, January 1, 2024. https://www.britannica.com/biography/Genghis-Khan.

Britannica, T. Editors of Encyclopaedia. "Ulaanbaatar." Encyclopedia Britannica. February 11, 2024. https://www.britannica.com/place/Ulaanbaatar.

Diener, Alexander and Joshua Hagen.  “City of Felt and Concrete: Negotiating Cultural Hybridity in Mongolia’s Capital of Ulaanbaatar”. Nationalities Papers 41, no. 4 (2013): 622-650.  DOI: 10.1080/00905992.2012.743513

Gomez, Basil, and John Paul Jones III. Research Methods in Geography. Chichester: Blackwell Publishing Ltd, 2010.

Monday, February 26, 2024

The Golden Chandelier

 We take flight on wings of glass,
 The bonnie sea dreaming ceaselessly,
 Your cat-shaped hair a tiara agleam,
 The pink sky washed by alpenglow.
 Diadem of winter parched on your temple
 That phases through the billowy cushions
 Aloft, a sprinkle of dawn on your pillow
 To kiss the auroras good night.
 Daylight enters the morning palanquin,
 Awakening our arabesque trellises,
 Coaxing them out of their slumber
 To lift us over the snowy mountains.
 Clouds rise with us to greet the crest,
 Where children fly upon white doves,
 Singing psalms to the mountain climbers,
 Scattered rays of joy upon their faces.
 Here there are roads paved by rainbows
 Leading to castles built on floating quartz,
 Freeways of the liberated transporting
 The ascended off the wind torn peaks.
 Together we are one, suspended in love,
 Entwined in laces of polar passion,
 Serenated by children, pianos of peace,
 Violins of stellar matrimony, in unison,
 Glowing inside a golden chandelier.

Thursday, February 8, 2024

Article Review: Spatial Correlation Between Traffic and Air Pollution in Beijing

Introduction

            Traffic related air pollution (TRAP) is thought to be an increasingly important problem in major cities like Beijing, China.  The researchers in this article wanted to find out if a new method would enhance and simplify the spatial relationship between traffic and air pollution.  By analyzing particulate matter (PM) 2.5 concentrations at various sites in the city, they were able to spatially plot the relationship by involving traffic monitoring information (Jiang et al 2019, 655).  Particulate matter, such as soot, ash, and sulfur oxide (ibid. 655) with a diameter greater than 2.5 micrometers has been shown to be toxic to humans, creating a higher risk for lung diseases when inhaled.  The researchers’ simplification of the methods involved in determining this relationship was not as successful as they concluded.

 

Literature Review

            Studies on the relationship between traffic and air pollution are prolific and consistently affirmative (ibid. 655).  Mathematical modeling has been the primary method of researching it, but the drawback is that it can only apply to observation sites (ibid. 655).  The researchers answered this problem by applying geostatistics and a grid-orientation method instead (ibid. 655).  This would ideally be a cheaper way of doing the research, producing better results.

 

Background and Geographical Context

The research was conducted in central Beijing, where air pollution is notoriously bad.  It has a population of over 20 million people and is surrounded by mountains (ibid. 659) that amplify stagnant air.  Time is another dimension of TRAP research.  The optimal time for TRAP research is during rush hour, which is from 5am to 9am and 5pm to 9pm in Beijing (ibid. 656).  This is when peak emissions from vehicles are most likely to occur, providing for research that is better supported by data.  The traffic data itself was retrieved from “car rental companies who installed location devices on all their vehicles” (ibid. 659).  A gridding system was used across central Beijing to record the results (ibid. 657).

 

Methods Used

            A statistical package involving spatial autocorrelation was implemented to explore the relationship between traffic and PM 2.5 levels.  Spatial autocorrelation is a measure of the dependency of data that is near an observation (Clifford et al 2016, 541).  Moran’s I is the mathematical tool used in spatial autocorrelation; it estimates the level of clustering in a set of data (ibid. 542) by measuring how similar the values are to those nearby.  It is a statistic commonly used to evaluate the significance of a relationship between variables.

            GIS is an optimal program for calculating spatial autocorrelation.  In addition to Moran’s I, it can generate various analyses with powerful precision.  One of these is called a hot spot analysis (ibid. 675-677), which plots on a map the areas of high correlation vs. the areas of low correlation.  Other GIS tools can then be used to enhance the analysis through gridding, which creates a raster of the data.

In the Beijing study, GIS was the primary vehicle of data processing.  First, a hot spot analysis was used to find the concentration of levels of traffic and PM 2.5 concentration (Jiang et al 2019, 657).  Gridding was then used to create a raster of the data based on values of the hot spot analysis (ibid. 657).  To create the grid, the Fishnet tool was used to construct polygons over the point features; Spatial Join counted the number of vehicles in each grid; and the Zonal Statistics tool calculated the grid’s average pixel values (ibid. 657).  Spatial autocorrelation was then needed to measure the significance of each grid cell’s value compared to its neighbors.  Moran’s I was then processed in GIS to extrapolate how clustered the cells were (ibid. 658).  Finally, a correlation between the two variables was calculated using the LISA method (ibid. 658).  This resulted in correlation maps showing each grid’s correlation value at certain times and days of the month.

 

Analysis and Discussion

            It was found that days of rain and strong wind significantly impacted PM 2.5 levels, making it less severe than on sunny days (ibid. 662).  The northwest area of Beijing had the worst TRAP on average, associating the highest levels of PM 2.5 with the highest traffic.  The southeast area of Beijing also showed a strong correlation, with the lowest levels of PM 2.5 being associated with the lightest traffic (ibid. 663).  The pattern would change on weekends, when there were fewer routine commutes to work and more random scattering.

            The researchers state that the most likely reason for there being a lower correlation in southwest Beijing is that the wind generally blows from north to south, making the PM 2.5 concentration higher while the traffic is relatively lower (ibid. 664).  Other areas had a lower correlation, like the south-central.  Several reasons are posited for why, but none of them are convincing.  The researchers favor there being other sources of PM 2.5 in the area than traffic congestion (ibid. 665), which is certainly plausible.  Possible sources of PM 2.5 range from vehicle exhaust to smoke from wildfires, factory emissions, dust and salt.  Based on findings from Karagulian et al (2015), only 25% of urban PM 2.5 comes from traffic, with another 15% coming from industrial activities, 20% by domestic fuel burning, 18% from dust and salt, and 22% from unspecified human activity.  Any of these factors could be skewing data from the Beijing study.

 

Conclusion

            When there isn’t a significant finding from a study, it’s important to state it in the abstract.  This saves the reader’s time and energy.  While it was stated that the methods could be a useful guide for future research, it doesn’t hold sway if the research doesn’t provide an important finding.  The researchers would have made a more powerful statement of the method if it had been successful.  You say more by setting the example, not the possibilities.

Bibliography

 

Clifford, Nicholas, Meghan Cope, Thomas Gillespie and Shaun French. Key Methods in Geography. Third edition. London: SAGE Publications Ltd, 2016.

Jiang, Lili, Ziheng Sun, Qingwen Qi, and An Zhang. “Spatial Correlation Between Traffic and Air Pollution in Beijing”. The Professional Geographer 71, no. 4 (2019): 654-657.

Karagulian, Federico, Claudio A. Belis, Carlos Francisco C. Dora, Annette M. Prüss-Ustün, Sophie Bonjour, Heather Adair-Rohani, and Markus Amann. “Contributions to cities' ambient particulate matter (PM): A systematic review of local source contributions at global level”. Atmospheric Environment 120 (2015): 475-483.  doi:https://doi.org/10.1016/j.atmosenv.2015.08.087.

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