旧金山字体_旧金山建筑业的兴衰。 施工趋势与历史
舊金山字體
This series of articles is devoted to the study of the construction activity of the main city of Silicon Valley — San Francisco. Charts and calculations were built with the help of Jupyter Notebook (Kaggle)
該系列文章專門研究硅谷主要城市舊金山的建筑活動。 借助Jupyter Notebook (Kaggle)構建圖表和計算
Data on more than a million building permits (records in two datasets) acquired from the San Francisco Construction Department allow us to analyze not only the construction activity in the city, but also critically examine the latest trends and development history of the construction industry over the past 40 years, from 1980 to 2019 (section “Annual Construction Activity in San Francisco”).
從舊金山建筑局獲得的超過一百萬份建筑許可的數據(兩個數據集中的記錄) 使我們不僅可以分析城市的建筑活動，而且可以嚴謹地檢查建筑業在該城市的最新趨勢和發展歷史。從1980年到2019年的40年(“舊金山年度建筑活動”部分)。
The movement of activity in the construction industry in San Francisco almost completely coincides with the growth schedule for gold and bitcoin (section “The future of the San Francisco construction industry, pattern prediction”)
舊金山建筑行業的活動變化幾乎與黃金和比特幣的增長時間表一致 (“舊金山建筑行業的未來，模式預測”部分)
Open data provides an opportunity to explore the main factors that have influenced and will have an effect on the development of the construction industry in the city, dividing them into “external” (economic booms and crises) and “internal” (the effect of holidays and seasonal-annual cycles).
開放數據為探討影響和將影響城市建筑業發展的主要因素提供了機會，將其分為“外部”(經濟繁榮和危機)和“內部”(假期的影響)和季節-年度周期)。
內容： (Content:)
1.打開數據并查看初始參數。 (1. Open data and overview of initial parameters.)
San Francisco building permit data — taken from the open data portal — data.sfgov.org. The portal has several datasets on the topic of construction. Two such datasets store and update data on permits issued for the construction or repair of facilities in the city:
舊金山建筑許可數據-從開放數據門戶獲取-data.sfgov.org。 該門戶網站具有關于建筑主題的多個數據集。 兩個這樣的數據集存儲和更新有關為城市中的設施的建設或維修而簽發的許可證的數據：
Building permits for the period 1980–2013 (850 thousand records)
1980-2013年的建筑許可 (85萬條記錄)
Building permits for the period after 2013 (280 thousand records, data are downloaded and updated weekly)
2013年之后的建筑許可 (28萬條記錄，每周下載和更新數據)
📓 These datasets contain information on the issued building permits with various characteristics of the facility for which the permit is issued. The total number of records (permits) received in the period 1980–2019 is 1,137,695 permits.
📓這些數據集包含有關已簽發建筑許可證的信息，這些信息具有簽發許可證的設施的各種特征。 1980-2019年期間收到的記錄(許可證) 總數為1,137,695個許可證。
The main parameters from this dataset that were used for analysis:permit_creation_date - date of creation of the permit (in fact, the day from which construction work begins)desctription - description of the permit (two or three keywords describing the construction (work) object for which permission was created)estimated_cost - estimated cost of construction workrevised_cost - cost of work after revaluation, increase or decrease of the initialvolume of the applicationexisting_use - type of housing (one-, two-family house, apartments, offices, etc.)
zipcode, location - zip code and coordinates of the object
Charts and calculations were built in the Jupyter Notebook (on the Kaggle.com platform).
圖表和計算是在Jupyter Notebook (在Kaggle.com平臺上)中構建的。
2.舊金山年度建筑活動 (2. Annual Construction Activity in San Francisco)
In the graph below, the data on the estimated_cost and revised_cost parameters is presented as a distribution of the total cost of work by month (in billion US dollars).
在下圖中，有關估計成本和修訂成本參數的數據表示為每月總工作成本的分布(十億美元)。
data_cost_m = data_cost.groupby(pd.Grouper(freq='M')).sum()📊 To reduce monthly “emissions”, monthly data is grouped by year. The graph of the amount of money invested over the years has received a more logical view and is amenable to analysis.
📊 為了減少每月的“排放量”，按年對每月數據進行分組 。 這些年來投資金額的圖表已獲得更合乎邏輯的觀點，并且可以進行分析。
data_cost_y = data_cost.groupby(pd.Grouper(freq='Y')).sum()By the annual movement of the sum of costs (all permits of the year) in urban facilities, it is seen that Economic factors from 1980 to 2019 have influenced the number and cost of construction projects or in other words, on San Francisco real estate investments.
通過城市設施成本總和的年度變動(一年中的所有許可)，可以看出1980年至2019年的經濟因素已經影響了建設項目的數量和成本，換句話說，對舊金山房地產投資產生了影響。 。
The number of building permits (the number of construction works or the number of investments) over the past 40 years has been closely related to economic activity in the Silicone Valley.
在過去40年中，建筑許可的數量(建筑工程數量或投資數量)與硅谷的經濟活動密切相關。
The first peak of construction activity was associated with the electronic hype of the mid-80s in the valley. The ensuing decline in electronics and banking in 1985 has led to the regional real estate market decline from which it has not yet recovered for nearly ten years.
建筑活動的第一個高峰與谷地80年代中期的電子宣傳有關。 隨之而來的是1985年電子和銀行業務的下滑，導致該地區的房地產市場下滑，從那時起，它已經有近十年沒有復蘇。
🎢 Thereafter, the construction industry in San Francisco went through a parabolic growth of several thousand percent before the collapse of the Dotcom bubble and the technological boom of recent years. It happened two more times — in 1993–2000 and 2009–2016.
🎢此后，在Dotcom泡沫破滅和近年來的技術繁榮之前， 舊金山的建筑業經歷了數千％的拋物線增長 。 它又發生了兩次-在1993–2000年和2009–2016年。
By removing the intermediate peaks and downturns and leaving the minimum and maximum values on each economic cycle, one can see how much large market fluctuations have plagued the industry over the past 40 years.
通過消除中間的高峰和低谷，并在每個經濟周期中保留最小值和最大值，您可以看到過去40年中有多少大的市場波動困擾著該行業。
The largest investment increase in the field of construction occurred during the dot-com boom, when during the period from 1993 to 2001, $ 10 billion, or about $ 1 billion a year, were invested in repairs and construction. If you count in square meters (the cost of 1 m2 in 1995 is $ 3,000) — this is approximately 350,000 m2 per year for 10 years, since 1993.
建筑領域投資增長最大的時期是網絡繁榮時期，當時從1993年到2001年，有100億美元，即每年約10億美元投資于維修和建筑。 如果以平方米計算(1995年1平方米的成本為3,000美元)-這是自1993年以來每年10年的大約350,000平方米。
The growth of annual total investments for this period amounted to 1215%.
在此期間，年度總投資增長了1215％ 。
Companies that leased construction equipment during this period were like people who sold shovels during the gold rush (in the same region in the middle of the 19th century). Only instead of shovels — in the 2000s there were already cranes and concrete pumps for the newly formed construction companies who wanted to make money on the construction boom.
在此期間租用建筑設備的公司就像在淘金熱期間(在19世紀中葉的同一地區)出售鐵鍬的人一樣。 僅用鏟子代替鏟子-在2000年代，已經出現了起重機和混凝土泵，供新成立的建筑公司使用，它們希望在建筑熱潮中賺錢。
After each crisis that the construction industry has experienced over the years, over the next two post-crisis years, investments (the number of applications for permits) in construction fell each time by at least 50%.
在經歷了建筑行業多年來的每一次危機之后，在接下來的兩個危機后的幾年里， 建筑業的投資(許可證的申請數量) 每次都至少下降了50％ 。
The largest crises in the construction industry in San Francisco occurred in the 90s, were with a frequency of 5 years, the industry either fell (-85% between 1983–1986), then rose again (+ 895% between 1988–1992), remaining on the same level in annual terms — 1981, 1986, 1988, 1993.
舊金山建筑業最大的危機發生在90年代，頻率為5年，該行業要么下跌(1983-1986年間為-85％)，然后又上升(1988-1992年間為+ 895％)，每年保持相同水平-1981、1986、1988、1993。
🌊 After 1993, all subsequent downturns in the construction industry amounted to no more than 50%. But the approaching economic crisis (due to COVID-19) could create a record crisis in the construction industry in the period 2017–2021, the fall of which already for the period 2017–2019 amounts to more than 60%.
1993 1993年以后，建筑業隨后的所有衰退不超過50％。 但是，即將到來的經濟危機(由于COVID-19)可能會在2017-2021年間造成建筑業創紀錄的危機，而 2017-2019年間的下降幅度已超過60％。
🏨 The population growth of San Francisco over the period 1980–1993 also showed almost exponential growth. The economic strength and innovative energy of Silicon Valley was the solid foundation upon which the hyperbole of the new economy, the American Renaissance and dotcoms was built. It was the epicenter of the new economy. But unlike the growth of real estate investments, after the peak of dotcoms, the population growth actually went to a plateau.
1980 1980年至1993年期間，舊金山的人口增長也顯示了近乎指數級的增長 。 硅谷的經濟實力和創新活力為新經濟，美國文藝復興時期和網絡公司的夸張奠定了堅實的基礎。 它是新經濟的中心。 但是，與房地產投資增長不同的是，在網絡泡沫破滅之后，人口增長實際上達到了一個穩定的水平。
Since the 1950s and before the peak of the dotcoms in 2001, the annual population growth has been approximately about 1% per year. Later, after a housing bubble pop included a downturn in the economy, the influx of a new population has slowed down and since 2001 it has only been 0.2 % per year.
自1950年代以來，互聯網泡沫在2001年達到頂峰之前， 每年的人口增長率約為1％ 。 后來，在房地產泡沫破滅包括經濟不景氣之后，新人口的涌入速度有所放緩， 自2001年以來，每年的涌入率僅為0.2％ 。
In 2019 (for the first time since 1950), the growth dynamics showed an outflow of the population (-0.21% or 7000 people) from the city of San Francisco.
在2019年(自1950年以來首次)，增長動力顯示舊金山市人口(-0.21％或7000人)外流。
3.編制估計費用的期望和現實 (3. Expectation and reality in drawing up the estimated cost)
In the used datasets, data on the cost of permitting a building object is divided into:
在使用的數據集中，關于建筑對象許可成本的數據分為：
initial estimated cost (estimated_cost)
初始估算費用( estimate_cost )
cost of work after revaluation (revised_cost)
重估后的工作成本( 修訂成本)
🎰 During the boom, the main purpose of revaluation is to increase the initial cost, when the investor (construction customer) shows a high interest in quality and volumes after the start of construction.
the在繁榮時期，當投資者(建筑客戶)在開始建造后對質量和數量表現出濃厚興趣時，重估的主要目的是增加初始成本。
During the crisis — they tried not to exceed the estimated cost and the initial estimates , practically trying not to undergo changes (with the exception of the 1989 earthquake).
在危機期間，他們試圖不超過估計的成本和最初的估計，實際上試圖不進行更改(1989年地震除外)。
According to the graph of the revalued and estimated cost built on the difference (revised_cost — estimated_cost), we can observe that:
根據基于差異(revised_cost —估計成本)的重估和估計成本的圖表，我們可以觀察到：
The amount of cost increase during the revaluation of the volume of construction work — directly depends on the cycles of the economic boom
重估建筑工程量期間的成本增加額-直接取決于經濟繁榮的周期
data_spread = data_cost.assign(spread = (data_cost.revised_cost-data_cost.estimated_cost))During periods of rapid economic growth customers (investors) spend their money generously enough, increasing their demands after the start of work.
在經濟快速增長的時期，客戶(投資者)足夠慷慨地花錢，開始工作后就增加了需求。
The customer (investor), feeling his financial confidence, asks the construction contractor or an architect to expand the already issued building permit. This may be a decision to increase the initial length of the pool or increase the area of the house (after the start of work and the issuance of a building permit).
客戶(投資者)感到自己的財務信心，要求建筑承包商或建筑師擴大已經簽發的建筑許可證。 這可能是增加游泳池的初始長度或增加房屋面積的決定(在開始工作并頒發建筑許可之后)。
At the peak of dotcoms, such “additional” expenses reached the “extra” 1 billion per year.
在互聯網高峰期，這種“額外”支出每年達到“額外” 10億美元。
If you look at this table as a percentage change, the peak increase in estimates (100% or 2 times the original estimated cost) came in the year before the earthquake in 1989 near the city. I suppose that after the earthquake (in 1989) the construction projects that were started in 1988 required more time and money to be implemented into it.
如果將此表看成是百分比變化，則估計最高峰值(100％或原始估計成本的2倍)出現在1989年地震發生前的一年。 我想在地震(1989年)之后，1988年開始的建設項目需要更多的時間和金錢來實施。
🌋 Conversely, a downward revision of the estimated cost (which happened only once during the period from 1980 to 2019) a few years before the earthquake is presumably due to the fact that some objects started in 1986–1987 were frozen or investments in these objects were cut back. According to the schedule, on average for each object begun in 1987, the estimated cost reduction was -20% of the original plan.
🌋相反，地震發生前幾年的估計成本(在1980年至2019年期間僅發生過一次)的向下修訂可能是由于1986-1987年開始的某些物品被凍結或對這些物品的投資被削減了。 根據時間表， 對于每個始于1987年的對象，估計的成本降低為原始計劃的-20％ 。
data_spread_percent = data_cost_y.assign(spread = ((data_cost_y.revised_cost-data_cost_y.estimated_cost)/data_cost_y.estimated_cost*100))The increase in the initial estimated cost by more than 40% indicated or possibly was the result of an approaching bubble in the financial and subsequently the construction market.
最初的估計成本增加了40％以上，這表明或可能是金融市場以及隨后的建筑市場泡沫逼近的結果。
What is the reason for the decrease in the spread (difference) between the estimated and revised sum after 2007?
2007年之后的估計數和修訂數之差(差異)減小的原因是什么？
Perhaps investors began to look at the numbers more carefully (the average investment over 20 years has increased from $ 100 thousand to $ 2 million dollars), or perhaps the construction department introduced new rules and restrictions to reduce possible manipulations and possible risks that arise during the crisis years in order to prevent and slow down the emerging bubbles in the real estate market.
也許投資者開始更仔細地研究數字(20年的平均投資已從10萬美元增加到200萬美元)，或者建筑部門出臺了新的規則和限制，以減少在操作過程中可能發生的操縱和可能出現的風險危機年代是為了防止和減緩房地產市場中出現的泡沫。
4.建筑活動取決于一年中的季節 (4. Construction activity depending on the season of the year)
Having grouped the data by calendar weeks in a year (54 weeks), you can observe the construction activity of the city of San Francisco, depending on seasonality and time of year.
在一年(54周)中按日歷周對數據進行分組后，您可以根據季節和一年中的時間觀察舊金山市的建筑活動。
🎅 By Christmas, all construction companies are trying to manage to get permission for new “large” objects (at the same time! The number! Permits in the same months are at the same level throughout the year). Investors, planning to get their property over the next year, conclude contracts in the winter months, counting on big discounts (since summer contracts, for the most part, are coming to an end by the end of the year and construction companies are interested in receiving new applications).
Christmas 到圣誕節之前，所有建筑公司都試圖設法獲得新的“大型”物品的許可 (同時！數量！一年中同一月份的許可處于同一水平)。 計劃在明年獲得財產的投資者，在冬季月份簽訂了合同，依靠大幅度的折扣(因為夏季合同大部分將在年底到期，而建筑公司對接收新的申請)。
Before Christmas, the largest amounts are submitted in applications (an increase from an average of 1–1.5 billion per month. Up to 5 billion in December alone). At the same time, the total number of applications by month remains at the same level (see the section below: Statistics on the total number of applications by month and days)
圣誕節之前，提交的申請數量最多 (從平均每月1–15億增加到12月份的50億)。 同時，每月申請總數保持在相同水平 (請參閱以下部分：按月份和天數統計的申請總數)
After the winter holidays, the construction industry is actively (almost without an increase in the number of permits) planning and implementing “Christmas” orders, so that by the middle of the year (before the Independence Day) have time to free up resources before the beginning of immediately after the June holidays — a new wave of summer agreements.
寒假過后，建筑行業正在積極(幾乎沒有增加許可證的數量)計劃和實施“圣誕節”命令，以便在年中(獨立日之前)有時間釋放資源六月假期后立即開始-夏季協議的新潮。
data_month_year = data_month_year.assign(week_year = data_month_year.permit_creation_date.dt.week)data_month_year = data_month_year.groupby(['week_year'])['estimated_cost'].sum()The same percentage data (orange line) also shows that the industry works “quietly” for a year, but before and after the holidays, permit activity increases to 150% between week 20–24 (before Independence Day), and decreases immediately after the holiday to -70%.
相同的百分比數據(橙色線)還表明，該行業“安靜”地工作了一年，但是在假期之前和之后，許可證活動在20-24周(獨立日之前)增加到150％，而在假期之后立即減少。假期到-70％。
Before Halloween and Christmas, activity in the construction industry in San Francisco week 43–44 increases by 150% (from bottom to peak) and then decreases to zero during the holidays.
在萬圣節和圣誕節之前，舊金山第43-44周的建筑業活動增加了150％(從底部到高峰)，然后在假日期間減少到零。
Therefore, the construction industry is in a six-month cycle, which is divided by the holidays “Independence Day of the USA” (week 20) and “Christmas” (week 52).
因此， 建筑行業以六個月為周期 ，除以假期“美國獨立日”(第20周)和“圣誕節”(第52周)。
5.舊金山房地產投資總額 (5. Total San Francisco Real Estate Investments)
Based on the data on building permits in the city:
根據城市建筑許可的數據：
The total investment in construction projects in San Francisco from 1980 to 2019 is $ 91.5 billion.
1980年至2019年，舊金山建設項目的總投資 為915億美元 。
sf_worth = data_location_lang_long.cost.sum()The total market value of all residential real estate in San Francisco, estimated by property tax (is the estimated value of all real estate and all personal property owned by San Francisco) has reached $ 208 billion in 2016.
通過財產稅估算的舊金山所有住宅房地產的總市值 (即舊金山擁有的所有房地產和所有個人財產的估算價值) 在2016年 已 達到2080億美元 。
6.在過去的40年中，舊金山在哪些地區進行了更多投資 (6. In which areas of San Francisco have invested more over the past 40 years)
With the help of the Folium library, let’s see where these $ 91.5 billion by regions were invested. To do this, grouping the data by zip code (zipcode), imagine the value obtained using circles (Circle function from the Folium library).
在Folium庫的幫助下，讓我們了解一下這915億美元的地區投資額。 為此，按郵政編碼(zipcode)對數據進行分組，想象一下使用圓獲得的值(來自Folium庫的Circle函數)。
import foliumfrom folium import Circle
from folium import Marker
from folium.features import DivIcon# map folium display
lat = data_location_lang_long.lat.mean()
long = data_location_lang_long.long.mean()
map1 = folium.Map(location = [lat, long], zoom_start = 12)for i in range(0,len(data_location_lang_long)):
Circle(
location = [data_location_lang_long.iloc[i]['lat'], data_location_lang_long.iloc[i]['long']],
radius= [data_location_lang_long.iloc[i]['cost']/20000000],
fill = True, fill_color='#cc0000',color='#cc0000').add_to(map1)
Marker(
[data_location_mean.iloc[i]['lat'], data_location_mean.iloc[i]['long']],
icon=DivIcon(
icon_size=(6000,3336),
icon_anchor=(0,0),
html='<div style="font-size: 14pt; text-shadow: 0 0 10px #fff, 0 0 10px #fff;; color: #000";"">%s</div>'
%("$ "+ str((data_location_lang_long.iloc[i]['cost']/1000000000).round()) + ' mlrd.'))).add_to(map1)
map1
By looking at districts, it becomes clear that the majority of investments went to DownTown. Having simplified the grouping of all objects according to the distance to the city center and the time needed to get to the city center (of course, expensive houses are also being built on the coast), all permissions were divided into 4 groups: ‘Downtown’, ‘<0.5H Downtown’, ‘< 1H Downtown ‘,’ Outside SF ‘.
通過查看地區，很明顯，大部分投資都投向了DownTown 。 根據到市中心的距離和到達市中心所需的時間簡化了所有對象的分組(當然，沿海地區也正在建造昂貴的房屋)，所有權限分為4組： ”，“ <0.5H市區”，“ <1H市區”，“ SF外”。
from geopy.distance import vincentydef distance_calc (row):
start = (row['lat'], row['long'])
stop = (37.7945742, -122.3999445) return vincenty(start, stop).meters/1000df_pr['distance'] = df_pr.apply (lambda row: distance_calc (row),axis=1)def downtown_proximity(dist):
'''
< 2 -> Near Downtown, >= 2, <4 -> <0.5H Downtown
>= 4, <6 -> <1H Downtown, >= 8 -> Outside SF
'''
if dist < 2:
return 'Downtown'
elif dist < 4:
return '<0.5H Downtown'
elif dist < 6:
return '<1H Downtown'
elif dist >= 6:
return 'Outside SF'
df_pr['downtown_proximity'] = df_pr.distance.apply(downtown_proximity)
91.5 billion that were invested in the city, almost 70 billion (75% of all investments) are invested in repairs and construction in the city center (green zone) and in the city area within a 2 km radius from the center (blue zone).
在該市投資了915億美元，其中將近700億(占投資總額的75％)在市中心 (綠色區域)和距市中心2公里半徑內的城市區域(藍色區域) 進行了維修和建設。 。
7.按市區劃分的建筑申請平均估計費用 (7. Average estimated cost of an application for construction by city district)
All data, as in the case of the total amount of investments, was grouped by zip code. Only in this case with the average (.mean ()) estimated cost of the application by zip code.
與總投資額一樣，所有數據均按郵政編碼分組。 僅在這種情況下，使用郵政編碼估算應用程序的平均成本(.mean())。
data_location_mean = data_location.groupby(['zipcode'])['lat','long','estimated_cost'].mean()In ordinary areas of the city (more than 2 km. From the city center) — the average estimated cost of an application for construction is $ 50 thousand.
在城市普通地區(距市中心2公里以上)，平均每份施工申請的估計費用為5萬美元。
The average estimated cost in the area of the city center is about three times higher ($ 150 thousand to $ 400 thousand) than in other areas ($ 30–50 thousand).
市中心區域 (15萬至40萬美元)的平均估計成本約為其他地區(3萬至5萬美元)的三倍。
In addition to the cost of land, three factors determine the total cost of housing construction: labor, materials, and government fees. These three components are higher in California than in the rest of the country. California building codes are considered among the most comprehensive and stringent in the country (due to earthquakes and environmental regulations), often requiring more expensive materials and labor.
除了土地成本外，三個因素還決定了房屋建設的總成本：人工，材料和政府費用。 這三個組成部分在加利福尼亞州高于全國其他地區。 由于地震和環境法規的原因， 加利福尼亞州的建筑法規被認為是美國最全面，最嚴格的法規，通常需要更昂貴的材料和勞動力。
For example, the State requires builders to use higher quality building materials (windows, insulation, heating and cooling systems) to achieve high standards in energy efficiency.
例如，紐約州要求建筑商使用更高質量的建筑材料(窗戶，隔熱，加熱和冷卻系統)以達到高能效標準。
From the general statistics on the average cost of an application for permission, two locations stand out favorably:
從有關許可平均費用的一般統計數據來看，有兩個地方比較突出：
Treasure Island — is an artificial island in the San Francisco Bay. The average estimated cost of a building permit is $ 6.5 million.
金銀島 -是舊金山灣的人工島。 建筑許可的平均估計費用為650萬美元。
Mission Bay — (lives 2926 people) The average estimated cost of a building permit is $ 1.5 million.
米森灣 ( Mission Bay) -(可容納2926人)建筑許可證的平均估計成本為150萬美元。
In fact, the highest average claim in these two areas is associated with the lowest number of applications for this zip code (145 and 3064 respectively, construction on the island is very limited), while for the rest of the postal codes for the period 1980–2019, approximately 1300 applications were received per year (total average of 30–50 thousand applications for the entire period).
實際上， 這兩個地區的平均索賠額最高，與此郵政編碼的申請數量最少 (分別為145和3064，島上的建筑非常有限)，而1980年其余的郵政編碼–2019年，每年大約收到1300份申請(整個期間平均平均35,000份申請)。
By the parameter “number of permits” is noticeable a perfectly even distribution of the number of applications per zip code throughout the city.
通過參數“許可證??數量”可以明顯看出，整個城市每個郵政編碼的申請數量分布非常均勻 。
8.按月和日統計的申請總數 (8. Statistics on the total number of applications by month and day)
General statistics on the number of applications by month and day from 1980 to 2019 shows that the “quietest” months for construction departments — are spring and winter months. At the same time, the amount of investments offered in the applications varies greatly, and it differs from month to month (see “Construction activity depending on the season of the year”). Among the days of the week on Monday, the department’s workload is approximately 20% less than the rest of the week.
根據1980年至2019年每月和每天的申請數量的一般統計數據顯示， 建筑部門“最安靜”的月份是Spring和冬季 。 同時， 應用程序中提供的投資金額差異很大，并且每個月都不相同 (請參閱“建筑活動取決于一年中的季節”)。 在星期一的一周中的幾天中，部門的工作量比一周中的其余時間減少大約20％。
data_month_count = data_month.groupby(['permit_creation_date']).count()While June and July practically do not differ in the number of applications, the difference in total estimated cost reaches 100% (4.3 billion in May and July and 8.2 billion in June).
雖然6月和7月的申請數量實際上沒有差異，但總估算成本的差異達到了100％(5月和7月為43億，6月為82億)。
data_month_sum = data_month.groupby(['permit_creation_date']).sum()9.舊金山建筑業的未來，模式預測。 (9. The future of the San Francisco construction industry, pattern prediction.)
In conclusion, we compare the graph of construction activity in San Francisco with the graph of Bitcoin prices (2015–2018) and the graph of gold prices (1940–1980)
總之，我們將舊金山的建筑活動圖與比特幣價格圖(2015–2018)和黃金價格圖(1940–1980)進行了比較。
Pattern — in technical analysis is a stable repeated combinations of price, volume or indicator data. Pattern analysis is based on one of the axioms of technical analysis: “history repeats itself” — it is believed that repeated combinations of data lead to a similar result. Technical analysts have long used price patterns to examine current movements and forecast future market movements.
模式 -技術分析中是價格，數量或指標數據的穩定重復組合。 模式分析基于技術分析的公理之一：“歷史重復本身” —相信重復的數據組合會產生相似的結果。 技術分析師長期以來一直使用價格模式來檢查當前走勢并預測未來市場走勢。
📈📉 Economic patterns have changed little from the ancient past to recent times. The main pattern that can be guessed on the annual activity chart is “Head and shoulders” — a trend reversal pattern. It is named because the graph looks like a human head (peak) and shoulders on the sides (smaller peaks). When the price breaks the line connecting the troughs, the pattern is considered complete, and the movement is likely to occur down.
📈📉 從古代到最近，經濟格局幾乎沒有變化。 年度活動圖表上可以猜到的主要模式是“頭和肩膀” ， 即趨勢反轉模式 。 之所以命名，是因為該圖看起來像人的頭(峰值)，而肩膀在側面(較小的峰)。 當價格跌破連接谷底的線時，該形態被認為是完整的，并且移動很可能發生。
The movement of activity in the construction industry in San Francisco almost completely coincides with the growth schedule for gold and bitcoin. The historical indicators of these three graphs of price and activity movement show significant similarities.
舊金山建筑業的活動變動與黃金和比特幣的增長時間表幾乎完全吻合 。 這三個價格和活動變動圖的歷史指標顯示出明顯的相似性。
In the future, it is necessary to calculate the correlation coefficient with each of these two trends. Two random variables are called correlated if their correlation moment (or correlation coefficient) is nonzero, and are called uncorrelated quantities if their correlation moment is zero. If the obtained value is closer to 0 than to 1, then talking about a clear pattern does not make sense. This is a difficult mathematical problem, which senior comrades may possibly take on, who may be interested in this topic.
將來，有必要計算這兩個趨勢的相關系數 。 如果兩個隨機變量的相關矩(或相關系數)不為零，則稱為相關變量；如果兩個相關變量的相關矩為零，則稱為不相關量。 如果獲得的值更接近于0而不是接近于1，則談論清晰的模式是沒有意義的。 這是一個很難解決的數學問題，對此同志可能會感興趣的高級同志可能會遇到。
🔮 !Unscientific! we can look at the topic of further development of the San Francisco construction industry through the similarity of patterns. If the pattern matches further with the price of bitcoin, then according to this pessimistic option — coming out of the crisis in the construction industry in San Francisco will not be easy for the near post-crisis time.
Un ！不科學！ 我們可以通過模式的相似性來探討舊金山建筑業的進一步發展主題。 如果這種模式與比特幣的價格進一步匹配，那么根據這種悲觀的選擇 ，在危機后的近段時間內擺脫舊金山建筑行業的危機將并不容易。
With a more “optimistic” development option, a repeated exponential growth of the construction industry is possible if activity here goes according to the “gold price” scenario. In this option, in 20–30 years (maybe in 10), the construction sector expects a new surge in employment and development.
如果采用“更樂觀”的發展選擇 ，那么根據“黃金價格”情景進行的活動可以使建筑業實現指數級增長。 在這種選擇下，建筑業預計在20至30年內(可能在10年內)將出現就業和發展的新趨勢。
In the next part, I will take a closer look at individual sectors of construction (repair of roofs, kitchens, construction of stairs, bathrooms, and if you wish — for industries or other data; please leave me a comment) and compare inflation for individual types of work with Fixed Mortgage Rates & US Treasury Yield.
在下一部分中，我將仔細研究各個建筑部門(修理屋頂，廚房，樓梯，浴室的結構，如果需要，請提供行業或其他數據；請給我評論)，并比較具有固定抵押貸款利率和美國國債收益率的各種類型的工作。
Link to Jupyter Notebook: San Francisco. Building sector 1980–2019.
鏈接到Jupyter Notebook： 舊金山。 建筑業1980–2019年 。
Please, those who are registered on Kaggle — put a plus to this Notebook (Thank you!)(Notebooks will later add code comments and explanations)
請在Kaggle上注冊的用戶在此筆記本上加一個號(謝謝！)(筆記本將在以后添加代碼注釋和解釋)
?? If you like my content, please consider buying me a coffee. Thank you for your support, I really appreciate it! buymeacoffee.com/boikoartem
??如果您喜歡我的內容，請考慮給我買一杯咖啡。 感謝您的支持，我真的很感激！ buymeacoffee.com/boikoartem
📈 More about various tools for working with big data visualization here:
here此處提供有關使用大數據可視化的各種工具的更多信息：
可視化。 大數據可視化工具 (Visualization. Big Data Visualization Tools)
You can learn more about working with Jupyter Notebook and about applying machine learning in construction:
您可以了解有關使用Jupyter Notebook以及在建筑中應用機器學習的更多信息：
價格和時間預測。 機器學習。 (Price and Time Prediction. Machine Learning.)
翻譯自: https://medium.com/swlh/the-ups-and-downs-of-the-san-francisco-construction-industry-23758beeb4f0
舊金山字體
總結
以上是生活随笔為你收集整理的旧金山字体_旧金山建筑业的兴衰。 施工趋势与历史的全部內容，希望文章能夠幫你解決所遇到的問題。
- 上一篇： 梦到蛇打什么奖周公解梦
- 下一篇： 梦到自己的手被蛇咬了好不好