Population in Czechia's Ski Resorts

The analysis provides a population estimate of municipalities with ski resorts in Czechia for an average Saturday in February 2022, selected as a typical peak season day. The results are based on a prediction model developed by VŠB-TUO. A ski resort is defined by the presence of an aerial way in the location. The content of this analysis can serve as a foundation for development projects and the delivery of innovative services in regions exposed to a significant number of visitors.

About

Data for this analysis are based on the custom model of Atlas of Population Mobility in Czechia¹. We used following indicators generated by the model.

• Commutes to location (MODEL). The variable indicates the number of people commuting to the given location (typically work or school, generally the destination where they spend the longest time in the daily cycle, which is the case of a location change for purpose of one day trip or beginning of the longer stay for holidays) from any commuting origin where they reside between 00:00:00 and 04:59:59 and which is situated outside of the location. The location is a polygon defined by the Location code. The value is estimate of VSB-TUO.
• Population in morning (MODEL). Estimated population in the morning. The value is estimate of VSB-TUO. We predict unique population that is present in the Location in time between 00:00:00 and 04:59:59.
• Usual population (CZSO 2019). Population reported by the government (Czech statistical office) for 2019².
• Nominal difference to usual population (MODEL). It is calculated as Population in the morning (MODEL) plus Commutes to location (MODEL) minus Usual population (CZSO 2019). This represents the number of estimated population which is in a given day (an average Saturday in February 2022) considered as guests in the location. It can interpreted as a potential total domestic market demand for the provision of services and products by the location. By default, this population includes also a seasonal workers in the location.
• Commutes from location (MODEL).Depending of a location specifics, Commutes from location (MODEL) can be considered in addition to this this indicator. Note than Nominal difference to usual population (MODEL) represents for a given average weekday information about unique population in the location within daily cycle. It is a nature of presented information that population in different categories may or may not meet at the same time in the location within a daily cycle. To separate the group a hourly break down analysis would have to be made. For an analysis in a standard depth this is not necessary.

For a better understanding of the methods used, we calculate data for a typical day, which, in this case, is an average Wednesday in February 2022. Using the model, we establish the population in the location for that single day, without considering any repetitions or recurrences. For example, if the model estimates 1000 people for a specific date in the location, and we are asked to estimate the total for the month, we would multiply 1000 by the number of days in the month (e.g., 28 days for February) assuming all days are alike. Therefore, the data provided in this analysis are for the single day unless stated otherwise.

The approach to interpreting and manipulating the result information assumes that the location needs to provide a single unit of service daily to the entire population present on a given day. However, since demand equality across all days of the week is not the case, we may adjust the presented data weights based on the average contributions of weekdays to obtain a more precise result if needed. See chapter [Progress of Population Sums on Weekdays][@progress-of-population-sums-on-weekdays].

It's important to note that the information provided by the model does not include data on foreign visitors. To paint a complete picture of the population in the location, additional data from other sources must be considered (such as Návštěvnost v hotelech 2023 vs. 2019 – 2022³, etc.). For some locations, estimates from 2019/2020 analytical reports can be used, as demonstrated in the chapter Consistence with Previous Analyses. Similar to time series predictions mentioned in the previous paragraph, methods can be applied to enhance the presented information in this area as well.

Ski Resorts Categories

Ski resorts categorization is done along following methods. Municipality, and Mountain Range ČÚZK⁴ are based on the aerial way origin OSM⁵. Each municipality is categorized based on a single category of ski resorts. If there is no aerial way starting in the municipality, the municipality remains unclassified. Classification is simplified. Interpretation with local knowledge is needed. The aerial way does not represent a ski resort in a minority of cases, such as Praha Petřín cable car or Stráž pod Ralskem a water ski lift.

A mountain range can host multiple ski resorts or municipalities. If a municipality spans across multiple mountain ranges, the new name of the mountain range is used, composed as a concatenated string of the original mountain range names.

The classification of ski resorts is conducted for municipalities according to the following rules:

• If the aerial way length sum is >= 1000 m and the usual population in the location is <= 5000, then it is categorized as ski_resorts_a_larger.
• If the aerial way length sum is < 1000 m, and the aerial way sum is >= 300 m, and the usual population in the location is <= 5000, then it is categorized as ski_resorts_b_smaller.
• If the aerial way sum length is < 300 m, and the usual population in the location is <= 5000, then it is categorized as ski_resorts_c_local.
• If the population in the location is > 5000, and the aerial way length sum is not equal to 0, then it is categorized as ski_resorts_d_in_larger_municipality. This category is not considered in analyses.

Table: Aerial Ways by Categories

ski_resort_category	Aerial Ways Count	Aerial Ways Length (m)	Mountains Count	Municipalities Count
ski_resorts_a_larger	174	109689	23	42
ski_resorts_b_smaller	126	43471	34	78
ski_resorts_c_local	39	7978	22	38
ski_resorts_d_in_larger_municipality	45	21679	20	26
Total	384	182817	99	184

Details can be found in the Ski resorts categories definition⁶.

Results

Out of the total 319,000 domestic guests in all ski resorts on Saturdays in February 2022, the larger ski resorts category in Czechia host 193,000 visitors, accounting for 60%.

Focusing on the Krkonoše region, larger ski resorts category there host 53,000 guests, representing 77% of all ski resorts in Krkonoše. There are 69,000 guests in Krkonoše, constituting 22% of the total domestic guests in Czechia´s ski resorts. If we consider the broader Krkonoše ski resorts, including Krkonošské podhůří with 22,000 guests, it adds an additional 7% share for the entire Krkonoše range. This brings the total share of domestic guests for Krkonoše range to almost 30%.

In focus to Krkonoše larger ski resorts analysis it might be surprising that the connection of Rokytnice nad Jizerou (10 thousand) and Harrachov (7 thousand) resorts could attract more guests than Špindlerův Mlýn (15 thousand). Consequently, it would secure the second place after the currently largest interconnected resorts of Pec pod Sněžkou (15 thousand), Jánské Lázně (7 thousand), Malá Úpa (7 thousand) and Černý Důl (3 thousand), with a total of approximately 32 thousand daily guests.

The results overview is based on the following reports, which can be retrieved in full following the referenced links.

• All Ski Resorts Data Saturdays February 2022⁷
  • Larger Ski Resorts Data Saturdays February 2022⁸
  • Smaller Ski Resorts Data Saturdays February 2022⁹
  • Local Ski Resorts Data Saturdays February 2022¹⁰

Data Validation and Completion

Consistence with Previous Analyses

In November 2020 an adhoc analysis of selected locations was prepared to determine unique population and divide it by roaming. The result was achieved within test of specific territories methods for the project ¹¹. Relevant information is summarized in the following table.

Table: Analysis of selected locations 2019/2020 - Harrachov

Location Code	Location Name	Unique population category	February 2019 [thousand]	February 2020 [thousand]	February 2019 [percent]	February 2020 [percent]
577081	Harrachov	Czech	200	225	61%	50%
577081	Harrachov	Foreign	130	225	39%	50%
577081	Harrachov	All unique	330	450	100%	100%

Table: Analysis of selected locations 2019/2020 - Rokytnice nad Jizerou

Location Code	Location Name	Unique population category	February 2019 [thousand]	February 2020 [thousand]	February 2019 [percent]	February 2020 [percent]
577456	Rokytnice nad Jizerou	Czech	270	300	82%	75%
577456	Rokytnice nad Jizerou	Foreign	60	100	18%	25%
577456	Rokytnice nad Jizerou	All unique	330	400	100%	100%

Table: Analysis of selected locations 2019/2020 - Špindlerův Mlýn

Location Code	Location Name	Unique population category	February 2019 [thousand]	February 2020 [thousand]	February 2019 [percent]	February 2020 [percent]
579742	Špindlerův Mlýn	Czech	290	340	69%	63%
579742	Špindlerův Mlýn	Foreign	130	200	31%	37%
579742	Špindlerův Mlýn	All unique	420	540	100%	100%

Actual analysis of ski resorts is based on the model Atlas of Population Mobility in Czechia. This does not include foreign population as stated earlier. We can however use knowledge gained in November 2020 to demonstrate how to complete the analysis to have information about overall population in selected locations, including foreigners. Other locations which were not a subject of November´s 2020 analysis can follow the similar method, provided the share of foreign visitors in the location is known from another data source. Apart of previously mentioned data³, information from credit cards payments in a location can be good source for this information. Information about a weight of an average February Saturdays population within an average week in February is available from the model of Atlas of Population Mobility in Czechia, which can be extracted for any location and any period of available modeled data.

Table: Analysis of selected locations February 2022

Location Code	577081	577456	579742
Location Name	Harrachov	Rokytnice nad Jizerou	Špindlerův Mlýn
Population in the morning (MODEL) 2022 February Saturdays [thousand]	5.33	7.96	6.96
Commutes to location (MODEL) 2022 February Saturdays [thousand]	3.38	4.74	9.11
Population usual (CZSO 2019) [thousand]	1.42	2.67	1.11
Population in the morning (MODEL) average 2022 February Saturdays wieght in week [percent]	13%	17%	19%
Commutes to location (MODEL) average 2022 February Saturdays wieght in week [percent]	26%	25%	25%
Month February 2022 Czech [thousand]	216	263	292
Month February 2022 Foreign (based on an average 201902, 202002 estimate) [thousand]	175	72	151
Month February 2022, Sum of daily present population [thousand]	391	336	443
... out of which Month February 2022, Sum of daily present Population usual (CZSO 2019) [thousand]	40	75	31
Month February 2022, Average of daily present population including foreigners [thousand]	14	12	16

The secondary effect of using the November 2020 analysis is to check whether the calculation methods provide concise results, despite the fact that another approach was adopted for ad-hoc analysis in November 2020 and the Atlas of Population Mobility in Czechia. By comparing the 2019, 2020, and 2022 population estimates, the results seem to be acceptable. An additional check can be done using reports from ski resorts operators to examine revenues in different seasons. However, a drawback of this method is the unknown number of transported passengers, actual ticket prices, and the number of rides per ticket and day.

The only conclusion regarding the population in various locations is that there was no significant change between the 2019, 2020, and 2022 seasons. It is noteworthy that February 2020 was not yet influenced by the COVID-19 pandemic, while February 2022 is far after pandemic.

Selected Economical Data of Ski Resorts Operators

Table: Selected Economical Data of Ski Resorts Operator in Špindlerův Mlýn (CZK thousand)

Resort	Item	2011	2012	2018	2019	2021	2022
Špindlerův Mlýn	Annual Report	201110-201209	201110-201209	201811-201910	201811-201910	202111-202210	202111-202210
Špindlerův Mlýn	Season	2010/2011	2011/2012	2017/2018	2018/2019	2020/2021	2021/2022
Špindlerův Mlýn	Total Assets (Net)	406,857	401,515	1,137,229	1,124,045	1,035,549	961,760
Špindlerův Mlýn	Long-Term Tangible Assets	373,683	353,725	804,479	787,106	744,408	707,462
Špindlerův Mlýn	Financial Result of the Current Accounting Period	27,307	23,608	59,051	51,491	(73,414)	33,238
Špindlerův Mlýn	Revenue from the Sale of Own Products and Services		215,580	414,789	422,517	76,751	546,931
Špindlerův Mlýn	Fares on Cable Cars and Lifts		194,890	290,527	298,829	40,546	293,545
Špindlerův Mlýn	Personal Expenses		44,003	66,613	73,116	43,414	68,018
Špindlerův Mlýn	Number of Employees		122	140	140	78	94

Table: Selected Economical Data of Ski Resorts Operator in Harrachov (CZK thousand)

Resort	Item	2011	2012	2018	2019	2021	2022
Harrachov	Annual Report	201110-201209	201110-201209	201810-201909	201810-201909	202110-202209	202110-202209
Harrachov	Season	2010/2011	2011/2012	2017/2018	2018/2019	2020/2021	2021/2022
Harrachov	Total Assets (Net)	196,000	195,000	250,362	263,859	243,121	254,892
Harrachov	Long-Term Tangible Assets	173,027	161,588	140,365	136,932	134,487	134,546
Harrachov	Financial Result of the Current Accounting Period	13,488	10,787	17,256	12,439	(20,061)	15,207
Harrachov	Revenue from the Sale of Own Products and Services	78,734	81,999	88,744	83,596	17,622	87,379
Harrachov	Fares on Cable Cars and Lifts	61,901	71,857	80,678	75,191	11,338	79,622
Harrachov	Personal Expenses	20,268	18,173	18,664	20,086	19,889	17,680
Harrachov	Number of Employees	46	59	39	42	39	41

Development of Ski Resorts Attendance Across Seasons and Weekdays

The calculation was carried out based on the annual report of the Pec pod Sněžkou ski resort 2004 - 2012.

Table: Revenues from Fares on Cable Cars and Lifts Pec pod Sněžkou 2010

Month	10/2004 - 9/2005	10/2005 - 9/2006	10/2006 - 9/2007	10/2007 - 9/2008	10/2008 - 9/2009	10/2009 - 9/2010	10/2010 - 9/2011	10/2011 - 9/2012
10	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.01
11	0.00	0.00	0.00	0.02	0.01	0.00	0.01	0.00
12	0.13	0.14	0.02	0.17	0.18	0.11	0.16	0.16
1	0.31	0.31	0.15	0.26	0.28	0.32	0.31	0.29
2	0.34	0.33	0.54	0.36	0.33	0.36	0.34	0.32
3	0.20	0.19	0.27	0.19	0.17	0.18	0.17	0.17
4	0.01	0.02	0.01	0.00	0.02	0.02	0.00	0.01
5	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.01
6	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
7	0.00	0.00	0.00	0.00	0.01	0.01	0.01	0.01
8	0.00	0.00	0.00	0.00	0.01	0.01	0.00	0.01
9	0.00	0.00	0.00	0.00	0.00	0.00	0.01	0.00
Total	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00
... out of which months 1,2	0.66	0.64	0.69	0.62	0.60	0.67	0.65	0.62
... out of which months 12,3	0.33	0.33	0.29	0.36	0.35	0.29	0.33	0.34
... out of which month other	0.01	0.03	0.02	0.03	0.05	0.04	0.03	0.05

Based on Pec pod Sněžkou ski resort´s data we may generalize distribution of revenues in the ski resorts as follows.

Progress of Population Sums on Weekdays

To estimate values of population presence in seasons, months and weekdays a detailed information from the model can be used.

References

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1. Atlas of population mobility in czechia. June 2023. Accessed on June 30, 2023. URL: https://danse.tech/atlas/results/. ↩

2. Počet obyvatel v obcích - k 1.1.2018. 3 2019. Accessed on June 30, 2023. URL: https://www.czso.cz/csu/czso/pocet-obyvatel-v-obcich-see2a5tx8j. ↩

3. Návštěvnost v hotelech 2023 vs. 2019 – 2022. January 2024. Accessed on January 8, 2024. URL: https://tourdata.cz/data/navstevnost-v-hotelech-2023-vs-2019-2022/. ↩↩

4. Geoportál čúzk. January 2024. Accessed on January 8, 2024. URL: https://geoportal.cuzk.cz/. ↩

5. Open street map. January 2024. Accessed on January 8, 2024. URL: https://www.openstreetmap.org/. ↩

6. Ski resorts categories definition. January 2023. Accessed on January 8, 2023. URL: https://medite-sss-infpro-182059100462.s3.eu-west-1.amazonaws.com/vsbtuo/ski_resorts_2024-01-07t18_04_31/source__standard_upload/ski_resorts_2024-01-07t18_04_31.pdf. ↩

7. All ski resorts data saturdays february 2022. January 2023. Accessed on January 8, 2023. URL: https://medite-sss-infpro-182059100462.s3.eu-west-1.amazonaws.com/vsbtuo/ski_resorts_populati_2024-01-07t17_59_46/source__standard_upload/ski_resorts_populati_2024-01-07t17_59_46.pdf. ↩

8. Larger ski resorts data saturdays february 2022. January 2023. Accessed on January 8, 2023. URL: https://medite-sss-infpro-182059100462.s3.eu-west-1.amazonaws.com/vsbtuo/ski_resorts_populati_2024-01-07t18_00_05/source__standard_upload/ski_resorts_populati_2024-01-07t18_00_05.pdf. ↩

9. Smaller ski resorts data saturdays february 2022. January 2023. Accessed on January 8, 2023. URL: https://medite-sss-infpro-182059100462.s3.eu-west-1.amazonaws.com/vsbtuo/ski_resorts_populati_2024-01-07t17_59_36/source__standard_upload/ski_resorts_populati_2024-01-07t17_59_36.pdf. ↩

10. Local ski resorts data saturdays february 2022. January 2023. Accessed on January 8, 2023. URL: https://medite-sss-infpro-182059100462.s3.eu-west-1.amazonaws.com/vsbtuo/ski_resorts_populati_2024-01-07t17_59_48/source__standard_upload/ski_resorts_populati_2024-01-07t17_59_48.pdf. ↩

11. Lokalizační data mobilních operátorů pro plánování města. 2019. Navštíveno 28. února, 2023. URL: https://smlouvy.gov.cz/smlouva/10905012. ↩