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Generate a string based on an integer range

Generate a string based on an integer range


After some initial research, I was unable to find a solution for the following issue. I'm sure there's an answer to this question already floating around here somewhere, but hopefully someone can give me a more personalized answer for my particular situation.

I am using ArcMap 10.3

I would like to have a String field that automatically generates a category name based off of an Integer field range.
Basically: If the Integer is between 0-500, the String would generate "Red" for the category field.
There are 5 different Categories to choose from within my Integer data.

To my understanding, this would need to happen via the Field Calculator, using a code block and Python.

So,
Can I generate a String result in Field1 based on a designated Integer range in Field2?
If so, How?


In the pre-logic script code block (after having chosen python as the parser), you'll want something similar to this -

def categorize(value): if 0 <= value <= 500: return 'red' elif 501 <= value <= 1000: return 'green' else: return 'light purple'

and in the text box below it, you'll want to call the functio nyou just defined, passing in field names as paramaters using !fieldname! notation like so -

categorize(!integer_field_name!)

You can use an Update Cursor to do this type of classification:

import arcpy fc = r'C:	empyourFC.shp' with arcpy.da.UpdateCursor(fc, ["Field1", "Field2"]) as cursor: for row in cursor: # row[0] = "Field1" # row[1] = "Field2" if 500 >= row[1] >= 0: row[0] = "red" elif 1000 >= row[1] > 500: row[0] = "green" elif 1500 >= row[1] > 1000: row[0] = "blue" else: row[0] = "unassigned" cursor.updateRow(row)


Yeah, you have a basic if/else logic, check out this: Basic If/Then in Python Parser of ArcGIS Field Calculator?

dim n if[integerfield] > 500 then n = 'Red' elseif[integerfield]…


Tourist spatiotemporal behavior is critical for the monitoring and planning of tourism destinations. This study focused on the tourist spatiotemporal behavior at a wine tourism destination, i.e., the eastern foot of Helan Mountain in Ningxia, traced tourist movement trajectories using a tracking app, and collected information on itineraries and tourist behavior characteristics through a questionnaire survey. A time-geographical analytical approach was used to analyze the tourist spatiotemporal movement patterns. Four spatiotemporal movement patterns were identified, and tourists’ demographic and behavioral characteristics were examined with these movement patterns. This study found that the spatial distribution of tourists was subject to the effects of spatial proximity, agglomeration, and transportation junctions. The results contribute to a better understanding of tourist behavioral characteristics under different spatiotemporal movement patterns in an emerging wine region. The implications for destination planning and marketing are discussed.

Qiushi Gu received her PhD from the School of Hotel and Tourism Management at the Hong Kong Polytechnic University. Dr. Gu is an associate professor in the Department of Tourism Studies in the School of Humanities at Southeast University, Nanjing, China. Her research interests include wine tourism, tourism geography, and consumer behavior. This research article is financed by her hosted grants, and she contributed to the drafting of the manuscript.

Haiping Zhang received his PhD degree in GIScience with the Key Laboratory of the Ministry of Education of Virtual Geographic Environment, Nanjing Normal University. His research interests include spatial analysis and geographic modeling, human behavior pattern mining and social culture quantitative analysis. He contributed to the article in the data analysis and the drafting of the methodology section.

Songshan (Sam) Huang is a research professor in tourism and service marketing in the School of Business and Law at Edith Cowan University. He obtained his PhD in Tourism Management from the Hong Kong Polytechnic University. His research interests include tourist behavior, destination marketing, tour guiding and various Chinese tourism and hospitality issues. He has published widely on Chinese tourist behavior and China's tourism and hospitality issues in major tourism and hospitality journals, including Tourism Management, Journal of Travel Research, International Journal of Hospitality Management, and Journal of Hospitality and Tourism Research. He contributed to the article in terms of the manuscript's structure and supervision.

Fang Zheng, PhD, is an associate professor at the College of Resources and Environmental Science at Ningxia University. Her research interests include tourism planning and urban planning. She contributed to the data collection and the drafting of the practical implications section.

Chongcheng Chen is a full-time professor at Fuzhou University. His research interests include spatial data mining and the geographical knowledge grid/cloud, spatial decision systems, geo-visualization and the virtual geographical environment, and cultural heritage documentation and tourism information services. He has coauthored more than 180 refereed conference and journal papers. He holds more than 16 China Patents & Software copyrights. He contributed to the article in terms of data collection, data analysis and supervision.


What is Scrapbook?

Scrapbook is a digital personal information manager. To understand what that means let's talk about some of the common information management tools we use each day. There are file storage services such as OneDrive, iCloud, and Dropbox, which we use to help manage photos and files, and email services like Gmail or Outlook, which we use to organize email, contacts, and calendar events. Facebook, LinkedIn, and Snapchat are social network services that facilitate communication and interaction between friends an contacts via news feeds and personal timelines. Twitter and blogging are further examples of information sharing platforms through which we relate what we're doing and what we've been thinking about. And, let's not forget the numerous offerings for personal wikis and journaling software.

All of these platforms manage your personal information in one form or another. Each has strengths and weaknesses and attempts to address a part of the personal information management problem. Scrapbook doesn't aim to replace any of these tools. In fact, Scrapbook can interface with these tools. That's nice you say, but then how is Scrapbook different? We created Scrapbook to deal with four concerns that we felt are not yet adequately addressed:

1. How to deal with archival data

Archival data is information that we don't need immediate access to, but may wish to review in the future. It includes, but is not limited to, year-end financial statements, old health records, newspaper clippings, postcards, brochures, tickets, invitations, and letters. Archival data is all of that physical paperwork that ends up in over-stuffed folders in drawers or file cabinets. Think of the times you needed to find something searching page by page through one bulging folder after another to finally locate (or not) what you were looking for. If your archival data happens to be already digitized, it's likely stored somewhere on a drive or in the cloud, but finding it can be a trick. The idea of not being able to locate important information at some point in the future bothered us, and we set out to address it with Scrapbook.

For years, we saved things like playbills of theater shows, mementos of places we visited, articles cut out of the paper, labels from products we liked, and other tidbits of information. We pasted the scraps of paper into blank notebooks, which eventually filled up. Our "scrapbooks" grew in number over the years. We noticed that we rarely referred back to these physical scrapbooks in large part, because except by chronological order, it was hard to find anything. You can think of Scrapbook, in part, as a digital version of the physical scrapbooks in which we preserved these scraps of information, but far more accessible and fun.

2. How to capture context about data

Metadata provides context about an item (event, place, person, or object) that we capture because it is important to understanding why the item is interesting in the first place. Contextual data gives meaning to and unlocks understanding of the event, place, person or object. But how to capture this metadata and where to persist it isn't obvious. For example, we keep personal notes on friends that include reminders about what they like to eat or are allergic to, the names of their kids, and important events in their lives. You might be thinking, why not store that in Outlook or Gmail contacts, and you'd be correct. But what about personal notes on books read? Or, thoughts about a special dinner, an epic hike, or a great concert? Where can we store all of these notes - context - consistently and in one place whether it's about an event, place, person, or object? Consistent management of contextual information (metadata) is one of the scenarios we set out to address in Scrapbook.

In the photos of pages from our physical scrapbooks, written notes can be seen around the pasted objects. These are examples of 'metadata' that provide necessary context.

It's ironic that using Bing or Google we can call up within seconds all the details of a celebrity. But what about someone we actually know or care about? We know what you're thinking: just go to Facebook and look them up. Yes, that sort of works if the person in question is even using Facebook. However, here we are talking about information that matters to us, which is information about someone that you probably wouldn't find in a social media profile or wouldn't even appear in Facebook. It's information that's gleaned by spending time with someone, in person, in the context of a direct relationship. How to access this kind of context quickly and on our terms intrigued us and became a guiding principle for Scrapbook.

We use people data here as an example, but our argument applies to all types of information, be it events, places, or objects. In a way, Scrapbook is a small private search engine customized for our data that can be searched quickly. How quickly? Less than 10 seconds in most cases. That might not sound that great at first, but think about that filing cabinet of over-stuffed folders. How long would it take to find something in there? Or navigating a half dozen different apps or web sites to find what you are looking for. With that in mind, 10 seconds isn't very bad at all.

4. How to own our information

Fundamentally, we don't have much trust in many of the platforms we've mentioned above, especially the current flavors of social network services. We are not keen on having our personal information exploited by algorithms to sell us products or filter our news. Yes, we tolerate this to a degree, but absolutely not for the broader categories of information we have in mind here. And while we acknowledge that these services are fun and constitute an important social component for many, it seems crazy to us that people spend time at all creating detailed personal timelines that generate ad sales for someone else.

We’re also not confident in the longevity of these services and platforms. Five years is a long time and ten years an eternity in the technology sector. MySpace, for example is a distant memory. Facebook is already regarded as passe, with internet newbies flocking now to Snapchat. Smaller platforms come and go in a relative twinkling. Returning for a moment to the concept of an archive, we’re thinking long term.

From the start, Scrapbook was envisioned as way we could maintain as much control as possible of our own data, and really, the story of our lives, while making it accessible in a secure, sustainable manner. That desire led us down the avenue of developing our own solutions. It's not an easy road, but it's been rewarding. We maintain that taking ownership of our data has given us a greater understanding and appreciation of what constitutes us data-wise, of the information we return to more often, and which data is important enough to save for the future.


When we work with financial institutions on Community Reinvestment Act (CRA) matters, we ask whether they have created a CRA plan. The answers we get range from &ldquoYes&rdquo to &ldquoNot yet, but it&rsquos on our list of things to do&rdquo to &ldquoNo, and it&rsquos not going to happen!&rdquo

While some banks feel they are doing just fine without the extra work, others may insist that unless required by their primary regulator for a special reason, no explicit directive exists to create and maintain a CRA Plan. Another argument circles the examination standard defense line, &ldquoIf we don&rsquot plan, the examiners cannot judge our performance against the plan and write us up if we fall short.&rdquo And, frankly, planning takes time. So why should the senior management team prepare a CRA Plan with these types of stumbling blocks in the way?

Consider Just the Basics

Starting with the basics is an important first step. For all sizes of financial institutions, initial planning efforts should focus on:

  • The bank&rsquos loan-to-deposit ratio being reasonable and adjusted for possible seasonal variations and other appropriate lending activities such as loan originations for sale to the secondary markets, community development loans, or qualified investments.
  • The majority percentage of the bank&rsquos loans and, as relevant, other lending-related activities being in its assessment area (AA).
  • The bank&rsquos record of lending to and engaging in other appropriate lending-related activities for borrowers of different income levels, business types, and farm sizes being reflective of reasonable penetration per the designated AA.
  • Geographic distribution of the bank&rsquos lending activities being reasonable in dispersion.
  • The bank&rsquos record of taking action, if warranted, in response to written complaints about its performance.

Examination Focus Areas

A CRA examination for a small bank focuses on the following factors:

Characteristics Indicators of Satisfactory Performance
Loan-to-Deposit Ratio (considering seasonal variations and taking into account lending-related activities) Reasonable given the bank&rsquos size, financial condition, and AA credit needs
Assessment Area(s) (AA) Concentration A majority of loan and other lending-related activities being in the AA
Borrower&rsquos Profile Reasonable penetration among individuals of different income (including low- to moderate-income (LMI)) levels and businesses and farms of different sizes
Geographic Distribution of Loans The geographic distribution of loans being reflective of reasonable dispersion throughout the AA
Response to Substantiated Complaints Appropriate action being taken by the bank in response to substantiated CRA complaints

If the board of directors&rsquo desired result is a satisfactory performance rating, the approach seems pretty straight forward. Plus, without any push for extra credit for more performance or extra efforts, it even sounds easy!

But What About . . .?

The &ldquowhat about&rdquo questions can come at any time, from an examiner, a director, or a member of senior management. And of course, the public can always send a written request to discuss the bank&rsquos CRA performance. Generally this type of scenario starts with one question, followed by a string of follow-up drill-down inquiries:

  • Is the loan-to-deposit ratio reasonable given the bank&rsquos size and financial condition, and the credit needs of the designated AA?
    • Historically, what has the ratio reflected?
    • Have any seasonal changes or yearly shifts occurred?
    • How does the bank compare to other institutions in the area?
    • Has the ratio changed since the last examination?
    • Based on historical analysis, what is the current trend? For example, if the AA ratio is slowly falling to 55% inside versus 45% outside the AA, and the previous average was 60% inside, the bank needs to explore the reasons for this. Seldom is there a singular event or issue behind this type of trend.
    • Is a quarterly analysis performed to assess lending trends by loan product in the designated AA? What are the historical trends of the bank versus its peers?
    • If the number and/or dollar amount of loans within the AA falls below 50% in the AA, have responsive options been identified?
    • What is deemed reasonable distribution for each institution?
    • Who is tracking this and performing the analysis, preferably on a quarterly basis but no less than semiannually?
    • Have there been changes in the AA demographics that might suggest different types of credit requests?
    • When reviewing the percentage of loans to low-income or moderate-income individuals, does the percentage of each activity reflect the demographics of the AA?
    • If data is based on location of loans, are loans made to LMI individuals or middle- and upper-income borrowers?
    • Does small business lending (and if applicable, farm lending) reflect the makeup of the AA?
    • If such a complaint has been received, has management taken specific steps to review and respond to the complaining party(ies)?
    • Have response times been reasonable?
    • Have mutually agreeable solutions been identified, discussed, and implemented?

    Pulling the Plan Together

    Now it&rsquos time to pull all the pieces together in a viable plan. An annual plan may be the first step since it&rsquos often the easiest one to project and it has the greatest relevance to the most recent year&rsquos (or years&rsquo) performance. Consider the following table as the first step in documenting desired performance.

    Assessment Area(s) (AA) Concentration

    Example: AA statistics are 6% low-income and 8% moderate income.

    The geographic distribution of loans being reflective of reasonable dispersion throughout the AA

    10% low-income and 15% moderate-income census tracts

    The next steps in planning may include:

    • Comparing last year&rsquos performance to peers&rsquo performance per last examination report.
    • For each category, graphing the performance over multiple years.
    • For each category, graphing performance for peers versus the bank over the last two exams.

    These ideas and others can provide information to help the bank gauge its next steps. Look for future insights to be shared exploring CRA planning for all sizes of institutions, with tips and resource links.

    Moving Forward

    While the annual plan is a first step in CRA management, many banks have expanded their vision to a multi-year plan. This can be particularly beneficial when also planning community development activities for lending, investments, and service.


    Named Individuals

    Cv.php?prenom= nathalie&nom= abadie ni back to ToC or Named Individual ToC

    IRI: http://recherche.ign.fr/labos/cogit/cv.php?prenom=Nathalie&nom=Abadie

    Http://www.eurecom.fr/

    IRI: http://www.eurecom.fr/

    Http://www.eurecom.fr/

    IRI: http://www.eurecom.fr/

    Ignf ni back to ToC or Named Individual ToC

    IRI: http://data.ign.fr/def/ignf


    Scale-Dependent Influences of Distance and Vegetation on the Composition of Aboveground and Belowground Tropical Fungal Communities

    Fungi provide essential ecosystem services and engage in a variety of symbiotic relationships with trees. In this study, we investigate the spatial relationship of trees and fungi at a community level. We characterized the spatial dynamics for above- and belowground fungi using a series of forest monitoring plots, at nested spatial scales, located in the tropical South Pacific, in Vanuatu. Fungal communities from different habitats were sampled using metagenomic analysis of the nuclear ribosomal ITS1 region. Fungal communities exhibited strong distance–decay of similarity across our entire sampling range (3–110,000 m) and also at small spatial scales (< 50 m). Unexpectedly, this pattern was inverted at an intermediate scale (3.7–26 km). At large scales (80–110 km), belowground and aboveground fungal communities responded inversely to increasing geographic distance. Aboveground fungal community turnover (beta diversity) was best explained, at all scales, by geographic distance. In contrast, belowground fungal community turnover was best explained by geographic distance at small scales and tree community composition at large scales. Fungal communities from various habitats respond differently to the influences of habitat and geographic distance. At large geographic distances (80–110 km), community turnover for aboveground fungi is better explained by spatial distance, whereas community turnover for belowground fungi is better explained by plant community turnover. Future syntheses of spatial dynamics among fungal communities must explicitly consider geographic scale to appropriately contextualize community turnover.

    This is a preview of subscription content, access via your institution.


    Animal Diversity Web

    Geographic Range

    Anguilla rostrata (Lesueur) is a catadromous species that spawns in the Atlantic Ocean and ascends streams and rivers in North and South America. Found in Atlantic, Great Lakes, Mississippi, the Gulf Basin, and south to South America. This species is more common near the sea rather than inland streams and lakes (Page & Burr, 1991).

    Habitat

    A. rostrata live in freshwater as adults, usually in larger rivers or lakes, primarily swimming near the bottom in search of food. The species prefers to hunt at night and resides in crevices or other shelter from the light during the day, often times burying themselves in the substrate, whether mud, sand or gravel (Landau, 1992).

    Physical Description

    Elongate, snakelike body with a small, pointed head. A. rostrata has no pelvic fins, but has one long dorsal fin that extends more than half of the body dorsal fin is continuous with the caudal and anal fin. The lower jaw projects beyond upper jaw. One small gill slit is found in front of each pectoral fin. Coloration is variable with maturity level, the larval stage is called a leptocephalus, or glass eel. This stage is transparent and leaf-shaped with a prominent black eye. The leptocephalus develops into an elver, characterized by a darker coloring, from gray to greenish brown (Page & Burr, 1991). The next stage, the yellow eel, is the adult form that lives in freshwater color ranges from yellow to olive-brown. Sexually mature adults, silver eels, are dark brown and gray dorsally, with a silver to white ventral side. Large eyes are prominent in silver eels. Individuals reach lengths up to 152 cm (Page & Burr, 1991).

    Reproduction

    A. rostrata is a catadromous species, living most of its life in freshwater, but spawning in saltwater (Sumich, 1999). Sexually mature adults migrate to the Sargasso Sea, to spawn and supposedly die. Eels may reside in freshwater systems for up to 20 years before leaving to spawn at sea. The female lays up to 4 million buoyant eggs, which are fertilized by the male. Despite the use of technologically advanced SONAR tracking methods, adult eels are yet to be conclusively observed or captured in the presumed spawning areas in the Sargasso Sea (Sumich, 1999).

    • Average age at sexual or reproductive maturity (female)
      Sex: female 1642 days AnAge
    • Average age at sexual or reproductive maturity (male)
      Sex: male 1642 days AnAge

    Lifespan/Longevity

    Behavior

    The catadromous behavior of A. rostrata leads to a diverse range of behaviors linked to the life cycle stage of the animal. The leptocephalus larvae drift toward coastal waters of North America for up to 18 months, developing into more avid carnivorous elvers upon reaching the coastal estuarine waters (NS Dept. of Fisheries website, 1999). All stages beyond the leptocephalus are voracious feeders, and aggressive swimmers, primarily active at night. A. rostrata exudes a prominent layer of slime over its entire body, making capture by hand very difficult. Large eels will actively bite with their fully toothed jaws when caught on hook and line. A. rostrata is capable of breathing through its skin along with its gills, and can endure several hours outside of water (NS Dept. of Fisheries website, 1999).

    Communication and Perception

    Food Habits

    Feeding habits of A. rostrata vary with level of maturity. The leptocephalus is planktivorous as it drifts to coastal waters and develops into an elver, which feeds on aquatic insects, small crustaceans, and dead fish (Landau, 1992). Yellow and Silver eels are primarily nocturnal carnivorous feeders, consuming insects, crustaceans, clams, worms, fish and frogs. Eels at this stage will also eat dead animal matter. Adult eels use rotational feeding to tear portions from prey by causing a twist in their bodies and spinning to generate force to remove pieces of food (Helfman et al., 1999). This behavior actually wastes large portions of food in eel aquaculture systems (Landau, 1992).

    Economic Importance for Humans: Positive

    Anguilla rostrata is of major economic importance. In Japan and Taiwan, elvers and adults are considered a delicacy and the elvers are also eaten live in Europe. The largest aquaculture of eels is in Japan, and then Europe and the United States to a lesser extent (Landau, 1992). All forms of A. rostrata , however, are sought after commercially, to be shipped to places where they are used as food. There is concern for A. rostrata populations in the United States recently because of over harvesting the elvers and glass eels so not enough eels are reaching adulthood to migrate back to the ocean and reproduce (NS Dept. of Fisheries website, 1999).

    Conservation Status

    Measures are now being taken to decrease the impact of fisheries on A. rostrata populations in the United States, such as more closely regulating harvesting of glass eels and elvers (Landau, 1992). Ongoing studies still track juveniles and adults during their time in freshwater and movements to the Sargasso Sea for spawning (Sumich, 1999).

    • IUCN Red List Not Evaluated
    • US Federal List No special status
    • CITES No special status
    • State of Michigan List No special status

    Contributors

    William Fink (editor), University of Michigan-Ann Arbor.

    Solomon David (author), University of Michigan-Ann Arbor.

    Glossary

    the body of water between Africa, Europe, the southern ocean (above 60 degrees south latitude), and the western hemisphere. It is the second largest ocean in the world after the Pacific Ocean.

    living in the Nearctic biogeographic province, the northern part of the New World. This includes Greenland, the Canadian Arctic islands, and all of the North American as far south as the highlands of central Mexico.

    having body symmetry such that the animal can be divided in one plane into two mirror-image halves. Animals with bilateral symmetry have dorsal and ventral sides, as well as anterior and posterior ends. Synapomorphy of the Bilateria.

    uses smells or other chemicals to communicate

    the nearshore aquatic habitats near a coast, or shoreline.

    having the capacity to move from one place to another.

    the area in which the animal is naturally found, the region in which it is endemic.

    uses touch to communicate

    References

    Helfman, G., B. Collette, D. Facey. 1999. The Diversity of Fishes . Malden, MA: Blackwell Science.

    Landau, M. 1992. Introduction to Aquaculture . New York: John Wiley & Sons Inc..

    NS Dept. of Fisheries, Dec. 29, 1999. "Department of Fisheries and Aquaculture, Nova Scotia" (On-line). Accessed October 29, 2000 at http://www.gov.ns.ca/fish/inland/species/eel.htm.

    Page, L., B. Burr. 1991. Peterson Field Guide to Freshwater Fishes . Boston: Houghton Mifflin Company.

    Sumich, J. 1999. An Introduction to the Biology of Marine Life . Boston: WCB/McGraw-Hill.


    Proactive neighbor localization based on distributed geographic table

    Purpose &ndash A large set of valuable applications, ranging from social networking to ambient intelligence, may see their effectiveness and appeal improved when supported by the large‐scale, real‐time tracking of mobile devices, either carried by humans or embedded into vehicles. A centralized approach, where few servers would collect position data and provide them to interested consumers, would hardly cope with the resource demand of the foreseen huge increase of users interested in location‐based services and with the flexibility needs of emerging user‐generated services. The purpose of this paper is to propose a decentralized peer‐to‐peer approach to cope with these requirements, for which positioning information flows directly among mobile devices incurring in limited data exchange. Design/methodology/approach &ndash The authors propose a decentralized peer‐to‐peer approach for which positioning information flows directly among mobile devices incurring limited data exchange. A peer‐to‐peer overlay scheme is introduced called distributed geographic table (DGT), where each participant can effectively retrieve node or resource information (data or service) located near any chosen geographic position. Next, the authors describe a DGT‐based localization protocol that allows each peer to proactively discover and track all peers that are geographically near to itself. Findings &ndash The authors provide a performance analysis of the protocol by simulating several 1,000 users that move across an urban area according to realistic mobility models. The results show that the solution is effective, robust, scalable and highly adaptable to different application scenarios. Originality/value &ndash The new contributions of this paper are a general framework called DGT, which defines a peer‐to‐peer strategy for mobile node localization, and a particular instance of the DGT that supports applications in which every node requires to be constantly updated about the location of its neighbors.

    Journal

    International Journal of Pervasive Computing and Communications &ndash Emerald Publishing

    Published: Sep 6, 2011

    Keywords: Peer‐to‐peer Neighbor position discovery Localization Mobile computing Location‐based services Information retrieval Information services Distributed systems


    Introduction and Motivation

    This post is about a personal information management system (PIM) - personal knowledge base (PKB) we call Scrapbook. In this post we will talk about our 15+ years of experience developing and implementing Scrapbook. But before we get into details, let's set the stage with some of the scenarios that prompted us to create Scrapbook.

    • We are about to walk into a friend's house. What are the names of her three children?
    • We remember a wonderful hot chocolate we had in Cuneo. What was the name of the café? What was the name of any place we ate at in Cuneo?
    • We are planning to to have a friend over for lunch and we want to prepare a dish we haven’t served her before and obviously want it to be something she’ll enjoy, so we review meals we’ve had with her in the past.
    • We are planning a dinner with friends and we are looking for an interesting red wine to serve. We remember that we drank some nice dolcettos from Piedmont last year. What were they?
    • We are about to call to make an appointment for a haircut. What are the names of the people that work at the salon so whoever answers the phone we're able to address them by name?
    • We are wondering about an upcoming wedding and want to review the invitation to confirm the date and protocol for gifts.
    • We are writing an email to a friend and we want to recommend the last hike we did in Piedmont. What were the details of that hike?
    • We remember reading an interesting book in 2010 that had "science" in the title. What was it?
    • We want to know if we've ever received a postcard from Germany.
    • We want to know the average cocoa fat percentage of chocolate we tend to buy and what, if any, is the correlation between percentage and how we rate it.
    • We want to understand which categories in Scrapbook have the most items as well as how many items there are per year.
    • Understand the power of developing your own personal information management system.
    • Consider collecting and managing your own personal data, by whatever means is appropriate for you. Your data tells your story and is too valuable to hand over to social media platforms to be exploited for their profit, and potentially at your expense.

    Show Me the Code

    If you are comfortable with coding or want to see the Scrapbook technical documentation, see the Travelmarx GitHub page. On the GitHub page you will find documentation and code for running a simplified version of Scrapbook called Scrapbook101 on the .NET Framework or .NET Core.


    Data Resource Profile: China Cohort Consortium (CCC)

    Yixin Sun and Zhengcun Pei contributed equally to this work.

    Yixin Sun, Zhengcun Pei, Houyu Zhao, Liming Li, Yonghua Hu, Luxia Zhang, Lan Wang, Yu Yang, Tao Huang, Siyan Zhan, for the China Cohort Consortium study group, Data Resource Profile: China Cohort Consortium (CCC), International Journal of Epidemiology, Volume 49, Issue 5, October 2020, Pages 1436–1436m, https://doi.org/10.1093/ije/dyaa102

    Large prospective cohort studies are an important method for investigating the association between potential risk factors and disease, especially exploring the potential slow-acting causes of common chronic diseases. 1 So far, a number of longitudinal cohort studies have been established in China, and some of them have similar research objectives but cover different populations and regions. To maximize the utility of publicly funded projects and increase the speed and reliability of scientific discovery, it is necessary to improve sharing and collaboration between multiple data sources. Therefore, the China Cohort Consortium (CCC) was initiated in October 2017.

    The aims of the CCC are (i) to provide information on data resources: the.


    Micro-Segmentation in the Age of Big Data

    One of the essential topics taught in introductory marketing courses is the concept of market segmentation, which is the division of a market into groups of consumers that share one or more characteristics. Although most marketers understand the value of segmentation as a means to better target their efforts, many are daunted by the task of applying segmentation in an age of Big Data, where a deluge of information—both internal and external—is available. In this post, I’ll quickly review traditional B2C and B2B approaches to segmentation and touch on the concepts of anti-segments before relaying story of how a retailer used micro-segmentation to significantly impact the probability of sale.

    B2C Segmentation in the Age of ‘Mad Men’

    If you took an introduction to Marketing class more than a decade ago, you were probably taught that there are four primary bases for segmentation: geographic (e.g. region, population growth or density), demographic (e.g. age, gender, education, income), psychographic (e.g. values, attitudes, lifestyles), and behavioral (e.g. usage patterns, price sensitivity).

    Although the recent popularity of the hit TV show ‘Mad Men’ may make some nostalgic for simplistic demographic segments like ‘housewives’ or ‘professionals,’ segmentation of B2C markets today is very granular and typically accounts for specific online and offline behaviors.

    B2B Segmentation is a Different Animal

    For business (B2B) markets, however, these variables are often less helpful. Traditionally, B2B marketers relied on SIC or NAISC codes to perform industry segmentation. However, these code systems are antiquated and new industries and business models—such as cloud services, SaaS and social media—are not represented.

    B2B segments are typically based on such factors as company size, buyer type, purchase criteria, etc. A recent survey by the Content Marketing Institute (CMI) and Outbrain of 1,416 business-to-business (B2B) marketers from North America ranked the frequency that B2B small business marketers targeted their content based on such characteristics (see chart).

    The advantage of segmentation schemes based on these traditional approaches is that they produce a relatively small number of easily understandable segments. The disadvantage is that they produce relatively broad segments that may have limited effectiveness.

    Anti-Segments (or Explicitly Defining Who You DON’T Want to Sell To)

    First introduced in The Lean Entrepreneur – How Visionaries Create Products, Innovate with New Ventures, and Disrupt Markets, an anti-segment is simply a market segment that you want to avoid selling to. As outlined by Brian Gladstein in a recent blog post titled “Turn Away a Customer? Yes… If They Are In Your Anti-Segment,” there are a number of reasons why this might be the case, including:

    • You can’t reach or service these customers profitably
    • They have product requirements that would consume your resources, making it impossible (or extremely difficult) to meet the needs of the rest of your customers
    • These customers don’t provide the upside that other segments do, in the form of longevity, up-sell opportunities, cross-sell opportunities, or other sources of revenue

    Why take the time to define anti-segments? If you don’t define your anti-segments, incorporate them in your internal sales and marketing training, and embed them in your targeting approach, you will find yourself squandering marketing dollars to move them through your funnel and wasting sales bandwidth to close them. It’s a classic problem where opportunistic sales may increase short-term revenue, but these customers and the accompanying lack of focus can ultimately sink the business.