Changes
On June 13, 2024 at 1:10:14 AM UTC, CleverMaps Data Catalog:
-
Updated description of Consumer Spending by Product Groups – Italy (Comuni) from
This dataset contains information about the kind od products that customers tend to spend for the most. You can purchase the dataset on the level of Comuni (7 904 units). This sort of dataset is also available in other European countries.
toThis dataset contains information about the kind of products that customers tend to spend for the most. You can purchase the dataset on the level of Comuni (7 904 units). This sort of dataset is also available in other European countries.
f | 1 | { | f | 1 | { |
2 | "author": "Franti\u0161ek Pavl\u00ed\u010dek", | 2 | "author": "Franti\u0161ek Pavl\u00ed\u010dek", | ||
3 | "author_email": "frantisek.pavlicek@clevermaps.io", | 3 | "author_email": "frantisek.pavlicek@clevermaps.io", | ||
4 | "availability": "Upon request", | 4 | "availability": "Upon request", | ||
5 | "category": "Geotypes/Consumer Segments", | 5 | "category": "Geotypes/Consumer Segments", | ||
6 | "coverage": "Italy", | 6 | "coverage": "Italy", | ||
7 | "creator_user_id": "e2185bb8-8a2a-457e-a273-2e757a7866d8", | 7 | "creator_user_id": "e2185bb8-8a2a-457e-a273-2e757a7866d8", | ||
8 | "crs": "No coordinate system", | 8 | "crs": "No coordinate system", | ||
9 | "data_source": "https://www.mb-research.de/", | 9 | "data_source": "https://www.mb-research.de/", | ||
10 | "dataset_dimension": "Dataset", | 10 | "dataset_dimension": "Dataset", | ||
11 | "date_validity": "2021-01-01", | 11 | "date_validity": "2021-01-01", | ||
12 | "dimension": | 12 | "dimension": | ||
13 | "https://secure.clevermaps.io/#/projectList/project/m30b90q331i3gio3", | 13 | "https://secure.clevermaps.io/#/projectList/project/m30b90q331i3gio3", | ||
14 | "for_sale": "For sale", | 14 | "for_sale": "For sale", | ||
15 | "geometry": "No geometry", | 15 | "geometry": "No geometry", | ||
16 | "groups": [], | 16 | "groups": [], | ||
17 | "id": "ebca977d-cd1a-4fe0-9a2f-ddf5e8f68031", | 17 | "id": "ebca977d-cd1a-4fe0-9a2f-ddf5e8f68031", | ||
18 | "isopen": true, | 18 | "isopen": true, | ||
19 | "license_id": "cc-zero", | 19 | "license_id": "cc-zero", | ||
20 | "license_title": "Creative Commons CCZero", | 20 | "license_title": "Creative Commons CCZero", | ||
21 | "license_url": "http://www.opendefinition.org/licenses/cc-zero", | 21 | "license_url": "http://www.opendefinition.org/licenses/cc-zero", | ||
22 | "lowest_granularity": "Municipality", | 22 | "lowest_granularity": "Municipality", | ||
23 | "maintainer": null, | 23 | "maintainer": null, | ||
24 | "maintainer_email": null, | 24 | "maintainer_email": null, | ||
25 | "metadata_created": "2023-03-01T13:31:58.377710", | 25 | "metadata_created": "2023-03-01T13:31:58.377710", | ||
n | 26 | "metadata_modified": "2023-11-07T00:30:40.275776", | n | 26 | "metadata_modified": "2024-06-13T01:10:13.981042", |
27 | "name": | 27 | "name": | ||
28 | "mb-research-consumer_spending_by_product_groups_-_italy_mb-research", | 28 | "mb-research-consumer_spending_by_product_groups_-_italy_mb-research", | ||
n | 29 | "notes": "This dataset contains information about the kind od | n | 29 | "notes": "This dataset contains information about the kind of |
30 | products that customers tend to spend for the most. You can purchase | 30 | products that customers tend to spend for the most. You can purchase | ||
31 | the dataset on the level of Comuni (7 904 units). This sort of dataset | 31 | the dataset on the level of Comuni (7 904 units). This sort of dataset | ||
32 | is also available in other European countries.", | 32 | is also available in other European countries.", | ||
33 | "num_resources": 1, | 33 | "num_resources": 1, | ||
34 | "num_tags": 5, | 34 | "num_tags": 5, | ||
35 | "organization": { | 35 | "organization": { | ||
36 | "approval_status": "approved", | 36 | "approval_status": "approved", | ||
37 | "created": "2023-01-29T16:55:22.474138", | 37 | "created": "2023-01-29T16:55:22.474138", | ||
38 | "description": "MB-Research is a company specializing itself in | 38 | "description": "MB-Research is a company specializing itself in | ||
39 | providing its users with economic data on different administrative and | 39 | providing its users with economic data on different administrative and | ||
40 | postal levels across Europe. The company mostly offers data about | 40 | postal levels across Europe. The company mostly offers data about | ||
41 | purchasing power, unemployment, demography and also series of | 41 | purchasing power, unemployment, demography and also series of | ||
42 | different packages derived from this data.", | 42 | different packages derived from this data.", | ||
43 | "id": "bea8ce14-5031-4087-ac7f-1e30b4395fbd", | 43 | "id": "bea8ce14-5031-4087-ac7f-1e30b4395fbd", | ||
44 | "image_url": | 44 | "image_url": | ||
45 | bs.twimg.com/profile_images/1275751395032006656/nQgmFA8I_400x400.jpg", | 45 | bs.twimg.com/profile_images/1275751395032006656/nQgmFA8I_400x400.jpg", | ||
46 | "is_organization": true, | 46 | "is_organization": true, | ||
47 | "name": "mb-research", | 47 | "name": "mb-research", | ||
48 | "state": "active", | 48 | "state": "active", | ||
49 | "title": "MB-Research", | 49 | "title": "MB-Research", | ||
50 | "type": "organization" | 50 | "type": "organization" | ||
51 | }, | 51 | }, | ||
52 | "owner_org": "bea8ce14-5031-4087-ac7f-1e30b4395fbd", | 52 | "owner_org": "bea8ce14-5031-4087-ac7f-1e30b4395fbd", | ||
53 | "price_notes": "Price EUR: 4700 \u20ac. Price CZK: price EUR x 25 x | 53 | "price_notes": "Price EUR: 4700 \u20ac. Price CZK: price EUR x 25 x | ||
54 | 1.2 (includes 20% margin). For all product groups. Also available for | 54 | 1.2 (includes 20% margin). For all product groups. Also available for | ||
55 | one product group for 2150 \u20ac, each additional product group is | 55 | one product group for 2150 \u20ac, each additional product group is | ||
56 | 650 \u20ac. Please find the relevant documents for price creation | 56 | 650 \u20ac. Please find the relevant documents for price creation | ||
57 | here: | 57 | here: | ||
58 | s://drive.google.com/drive/folders/18yCEyUhl-_0-tzXQ5uxMRCOx56B4dHTp", | 58 | s://drive.google.com/drive/folders/18yCEyUhl-_0-tzXQ5uxMRCOx56B4dHTp", | ||
59 | "private": false, | 59 | "private": false, | ||
60 | "relationships_as_object": [], | 60 | "relationships_as_object": [], | ||
61 | "relationships_as_subject": [], | 61 | "relationships_as_subject": [], | ||
62 | "resources": [ | 62 | "resources": [ | ||
63 | { | 63 | { | ||
64 | "cache_last_updated": null, | 64 | "cache_last_updated": null, | ||
65 | "cache_url": null, | 65 | "cache_url": null, | ||
66 | "ckan_url": "https://datacatalog.ps.cleveranalytics.com", | 66 | "ckan_url": "https://datacatalog.ps.cleveranalytics.com", | ||
67 | "created": "2022-06-10T06:55:28.621966", | 67 | "created": "2022-06-10T06:55:28.621966", | ||
68 | "datastore_active": true, | 68 | "datastore_active": true, | ||
69 | "datastore_contains_all_records_of_source_file": false, | 69 | "datastore_contains_all_records_of_source_file": false, | ||
70 | "description": "Data sample", | 70 | "description": "Data sample", | ||
71 | "format": "CSV", | 71 | "format": "CSV", | ||
72 | "hash": "11e707d829d96462ca4a9a3320617178", | 72 | "hash": "11e707d829d96462ca4a9a3320617178", | ||
73 | "id": "d3c61037-1c84-4663-80c5-e64fceead868", | 73 | "id": "d3c61037-1c84-4663-80c5-e64fceead868", | ||
74 | "ignore_hash": true, | 74 | "ignore_hash": true, | ||
75 | "last_modified": null, | 75 | "last_modified": null, | ||
76 | "map": | 76 | "map": | ||
77 | nsumer_spending_view?token=he2w0Y3gnLF1hFKufYiw1xqUv7ZKNvidPFaMOwyr2e0 | 77 | nsumer_spending_view?token=he2w0Y3gnLF1hFKufYiw1xqUv7ZKNvidPFaMOwyr2e0 | ||
78 | ", | 78 | ", | ||
t | 79 | "metadata_modified": "2023-11-07T00:30:40.279443", | t | 79 | "metadata_modified": "2024-06-13T01:10:13.985101", |
80 | "mimetype": null, | 80 | "mimetype": null, | ||
81 | "mimetype_inner": null, | 81 | "mimetype_inner": null, | ||
82 | "name": "Consumer Spending by Product Groups", | 82 | "name": "Consumer Spending by Product Groups", | ||
83 | "original_url": | 83 | "original_url": | ||
84 | 3/data_partners/mb_research/mb_research_sample_consumer_spending.csv", | 84 | 3/data_partners/mb_research/mb_research_sample_consumer_spending.csv", | ||
85 | "package_id": "ebca977d-cd1a-4fe0-9a2f-ddf5e8f68031", | 85 | "package_id": "ebca977d-cd1a-4fe0-9a2f-ddf5e8f68031", | ||
86 | "position": 0, | 86 | "position": 0, | ||
87 | "resource_id": "d3c61037-1c84-4663-80c5-e64fceead868", | 87 | "resource_id": "d3c61037-1c84-4663-80c5-e64fceead868", | ||
88 | "resource_type": null, | 88 | "resource_type": null, | ||
89 | "s3link": | 89 | "s3link": | ||
90 | g/data_partners/mb_research/mb_research_sample_consumer_spending.csv", | 90 | g/data_partners/mb_research/mb_research_sample_consumer_spending.csv", | ||
91 | "set_url_type": false, | 91 | "set_url_type": false, | ||
92 | "size": null, | 92 | "size": null, | ||
93 | "state": "active", | 93 | "state": "active", | ||
94 | "task_created": "2023-03-16 10:54:11.266679", | 94 | "task_created": "2023-03-16 10:54:11.266679", | ||
95 | "url": | 95 | "url": | ||
96 | 3/data_partners/mb_research/mb_research_sample_consumer_spending.csv", | 96 | 3/data_partners/mb_research/mb_research_sample_consumer_spending.csv", | ||
97 | "url_type": null | 97 | "url_type": null | ||
98 | } | 98 | } | ||
99 | ], | 99 | ], | ||
100 | "state": "active", | 100 | "state": "active", | ||
101 | "tags": [ | 101 | "tags": [ | ||
102 | { | 102 | { | ||
103 | "display_name": "Italy", | 103 | "display_name": "Italy", | ||
104 | "id": "1b89e738-0463-4bf0-9307-191c6ed3a684", | 104 | "id": "1b89e738-0463-4bf0-9307-191c6ed3a684", | ||
105 | "name": "Italy", | 105 | "name": "Italy", | ||
106 | "state": "active", | 106 | "state": "active", | ||
107 | "vocabulary_id": null | 107 | "vocabulary_id": null | ||
108 | }, | 108 | }, | ||
109 | { | 109 | { | ||
110 | "display_name": "MB-Research", | 110 | "display_name": "MB-Research", | ||
111 | "id": "6d777b9b-664b-4660-8e8f-3904192e3e09", | 111 | "id": "6d777b9b-664b-4660-8e8f-3904192e3e09", | ||
112 | "name": "MB-Research", | 112 | "name": "MB-Research", | ||
113 | "state": "active", | 113 | "state": "active", | ||
114 | "vocabulary_id": null | 114 | "vocabulary_id": null | ||
115 | }, | 115 | }, | ||
116 | { | 116 | { | ||
117 | "display_name": "consumer spending", | 117 | "display_name": "consumer spending", | ||
118 | "id": "49b4c136-c590-4847-abd3-6eac683728c8", | 118 | "id": "49b4c136-c590-4847-abd3-6eac683728c8", | ||
119 | "name": "consumer spending", | 119 | "name": "consumer spending", | ||
120 | "state": "active", | 120 | "state": "active", | ||
121 | "vocabulary_id": null | 121 | "vocabulary_id": null | ||
122 | }, | 122 | }, | ||
123 | { | 123 | { | ||
124 | "display_name": "consumers", | 124 | "display_name": "consumers", | ||
125 | "id": "7f3db544-d578-436b-9811-a3b3e4a23dcd", | 125 | "id": "7f3db544-d578-436b-9811-a3b3e4a23dcd", | ||
126 | "name": "consumers", | 126 | "name": "consumers", | ||
127 | "state": "active", | 127 | "state": "active", | ||
128 | "vocabulary_id": null | 128 | "vocabulary_id": null | ||
129 | }, | 129 | }, | ||
130 | { | 130 | { | ||
131 | "display_name": "partner", | 131 | "display_name": "partner", | ||
132 | "id": "59088a3f-832b-421c-9601-aa41a3373508", | 132 | "id": "59088a3f-832b-421c-9601-aa41a3373508", | ||
133 | "name": "partner", | 133 | "name": "partner", | ||
134 | "state": "active", | 134 | "state": "active", | ||
135 | "vocabulary_id": null | 135 | "vocabulary_id": null | ||
136 | } | 136 | } | ||
137 | ], | 137 | ], | ||
138 | "title": "Consumer Spending by Product Groups \u2013 Italy | 138 | "title": "Consumer Spending by Product Groups \u2013 Italy | ||
139 | (Comuni)", | 139 | (Comuni)", | ||
140 | "type": "dataset", | 140 | "type": "dataset", | ||
141 | "url": null, | 141 | "url": null, | ||
142 | "use_cases": | 142 | "use_cases": | ||
143 | telligence-market-research,https://www.clevermaps.io/site-evaluation", | 143 | telligence-market-research,https://www.clevermaps.io/site-evaluation", | ||
144 | "use_cases_description": "Finding out the real catchment areas of | 144 | "use_cases_description": "Finding out the real catchment areas of | ||
145 | your branches and comparing them with driving or walking catchment | 145 | your branches and comparing them with driving or walking catchment | ||
146 | areas can help you optimizing the locations of your branches.,Locate | 146 | areas can help you optimizing the locations of your branches.,Locate | ||
147 | the places with an insufficient coverage based on data about | 147 | the places with an insufficient coverage based on data about | ||
148 | population. Evaluate planned locations and find out straightaway what | 148 | population. Evaluate planned locations and find out straightaway what | ||
149 | amount of people will you cover.,Analyze your current market share on | 149 | amount of people will you cover.,Analyze your current market share on | ||
150 | a spatial level to focus your expansion efforts on the areas with a | 150 | a spatial level to focus your expansion efforts on the areas with a | ||
151 | potential customer base and a beneficial number of | 151 | potential customer base and a beneficial number of | ||
152 | competitors.,Analyze the presence of your competition and include the | 152 | competitors.,Analyze the presence of your competition and include the | ||
153 | findings in the business strategy. Location data will help you to try | 153 | findings in the business strategy. Location data will help you to try | ||
154 | targeting areas with more potential.,Evaluate\u00a0the retail | 154 | targeting areas with more potential.,Evaluate\u00a0the retail | ||
155 | potential of any address and measure its attractiveness within its | 155 | potential of any address and measure its attractiveness within its | ||
156 | natural catchment area.\u00a0Choosing the right place for you business | 156 | natural catchment area.\u00a0Choosing the right place for you business | ||
157 | has never been easier.", | 157 | has never been easier.", | ||
158 | "version": null, | 158 | "version": null, | ||
159 | "visualization": "Polygon" | 159 | "visualization": "Polygon" | ||
160 | } | 160 | } |